FullStack

Build and deploy AI workflows and scalable enterprise integrations while leading technical delivery for client projects. | Requires 5+ years of software development, production AI experience, hands-on LangGraph expertise, enterprise configuration API experience, and a four-year degree. | About FullStack FullStack is your AI-native engineering partner, built to turn AI capability into certainty. Most companies can demo AI; few can get it to production and prove what it produced. We close that gap with three capabilities, AI built into your products from the start, elite vetted talent who apply it, and transparent execution that shows value every step of the way. With now over 600 customers in North America, FullStack is one integrated partner for all your AI engineering needs. With FullStack, you move forward with confidence. We’re Most Proud Of • Offering life-changing career opportunities to talented software professionals across the Americas. • Building highly-skilled software development teams for hundreds of the world’s greatest companies. • Having delivered hundreds of successful custom software solutions, which have positively impacted the lives and careers of millions of users. • Our 4.1-star rating on GlassDoor. • Our client Net Promoter Score of 68, twice the industry average. The Position We're Looking To Hire a Senior AI Software Engineer (LangGraph) To Join Our Team. You'll Work With Our Incredible Clients In One Of Two Ways • Team Augmentation: You will integrate yourself directly into our client's team and work alongside their existing designers and engineers on a daily basis. • Design & Build: You will work on a FullStack product team to build and deliver a product to our clients. What We're Looking For • 5+ years of professional software development and Enterprise Configuration APIs. • Advanced English is required. • Successful completion of a four-year college degree is required. • 1-4 years of experience designing, developing, and deploying AI-driven applications and workflows in production. • Hands-on expertise with LangGraph, including building, orchestrating, and managing complex AI workflows. • Strong backend software engineering skills, with experience designing scalable services, APIs, and enterprise application integrations. • Extensive experience with at least one enterprise ecosystem: Workday (WWS/SOAP, REST, EIB), Oracle Fusion (HDL, FSM, FBDI, REST), SAP SuccessFactors (OData v2/v4, MDF), or UKG Pro WFM. • Strong history of technical leadership, specifically in leading code reviews and managing daily offshore technical handoffs. • Demonstrated experience in effectively communicating complex architectural concepts and project progress to senior leadership. • Proactive, ownership-driven mindset capable of balancing high-level strategy with deep, hands-on coding. • Ability to work through new and difficult issues and contribute to libraries as needed. • Ability to create and maintain continuous integration and delivery of applications. • Forensic attention to detail. • A positive mindset and a can-do attitude. • Experience working on Agile/Scrum teams. • Meaningful experience working on large, complex systems. • Ability to take extreme ownership over your work. Every day is a challenge to ensure you are performing to the expectations you and your team have agreed upon. • Ability to identify with the goals of FullStack's clients, and dedicate yourself to delivering on the commitments you and your team make to them. Nice To Have We’re Seeking Engineers Who Are AI-enthusiastic In Their Workflow. Professional Experience Or Personal Project Work With The Following Is a Significant Plus • Cursor, Windsurf, or Google Antigravity. • Claude Code, Gemini CLI, Codex, or Copilot CLI. Work Authorization Requirements • Applicants must be currently authorized to work in the United States on a full-time basis. • FullStack will not sponsor applicants for work visas now or in the future. What We Offer • Competitive Salary. • Paid Time Off (vacation, sick leave, parental leave, and holidays). • 100% remote work. • The ability to work with leading startups and Fortune 500 companies. • Health, dental, and vision insurance. • 401(k) w/ 4% match. • Ample opportunity for career advancement. • Continuing education opportunities. FullStack is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. Learn more about our Applicants Privacy Notice.

Lead client discovery, architect and build production AI-enabled systems, and own their integration and delivery pipelines. | Requires 5+ years of software development, 1–4 years shipping LLM or agentic systems, client-facing discovery and consulting, CI/CD ownership, and a four-year degree. | About FullStack FullStack is your AI-native engineering partner, built to turn AI capability into certainty. Most companies can demo AI; few can get it to production and prove what it produced. We close that gap with three capabilities, AI built into your products from the start, elite vetted talent who apply it, and transparent execution that shows value every step of the way. With now over 600 customers in North America, FullStack is one integrated partner for all your AI engineering needs. With FullStack, you move forward with confidence. We’re Most Proud Of • Offering life-changing career opportunities to talented software professionals across the Americas. • Building highly-skilled software development teams for hundreds of the world’s greatest companies. • Having delivered hundreds of successful custom software solutions, which have positively impacted the lives and careers of millions of users. • Our 4.1-star rating on GlassDoor. • Our client Net Promoter Score of 68, twice the industry average. The Position Forward Deployed Engineers sit inside the client’s problem, not next to it. You embed with an enterprise team, find the work that actually warrants AI, architect the system, build it, and make it hold up in the real environment. This is a three-way role: consultant, operator, engineer. You should be as comfortable pressure-testing a business case with a COO or VP of Engineering as you are debugging why an agent’s tool-calling loop degraded after a context change. The people who succeed here can hold a discovery conversation on Monday and ship what they scoped on Thursday. On this track, your edge is application engineering: you make agents real inside the client’s actual systems, workflows, and delivery pipelines. We work with regulated industries and Fortune 500 clients who have AI mandates, real constraints, and low tolerance for demos that don’t hold up. How We Approach The Work FDEs are trusted to shape the solution, not only deliver it. Every engagement calls for architect-level judgment: • Reframes the request. Treats “we need an agent for X” as a hypothesis, not a spec. • Decomposes the problem. Outcome → process → decisions → data → systems → actors → constraints — before choosing technology. • Chooses the intervention. Remove, simplify, traditional software, automation, LLM, RAG, agent, or multi-agent — and defends why. • Owns the architecture decision. Documents trade-offs (ADRs), sets success metrics, pushes back on scope that won’t deliver value. Consultative Discovery Clients rarely arrive with a fully formed problem, and the FDE’s first contribution is helping them see it clearly. You lead discovery with genuine curiosity, moving the conversation from stated requirements to underlying needs, and you build credibility through the depth of your understanding rather than the breadth of your pitch. • Uncovers business drivers. What outcome matters, what it’s worth, why now, and what happens if nothing changes. • Uncovers personal drivers. What each stakeholder is measured on, what they’re worried about, and what a win looks like for them individually. • Surfaces constraints early. Security, compliance, data access, budget, skills, politics, timelines — the things that kill projects in month two. • Aligns stakeholders. Spots conflicting goals between business, engineering, and risk, and brings them to a shared definition of success. • Asks strategic questions. Follows the answer with “why,” “how do you know,” and “what happens today when…” — without interrogating. Clients leave the conversation understanding their own problem better. • Earns authority through depth. Credibility comes from the quality of the questions and the insight in the playback, not from pitching technology. What You'll Do • Discover. Run workshops and process discovery with business and engineering stakeholders. Separate genuine AI problems from workflow problems wearing an AI costume. Come back with a scoped, defensible point of view. • Structure the process and the spec. For any business or engineering process being automated, map the current state, decision points, and exceptions, then turn it into specifications that both people and AI systems can execute against reliably. • Architect & design. Design agentic systems end to end — orchestration, context and retrieval architecture, tool and MCP integration, guardrails, human-in-the-loop checkpoints, and the boundaries between deterministic and non-deterministic components. • Build. Ship working systems — agents, multi-step automations, internal tooling, integration layers. You are hands-on. Prototypes that prove value in weeks, not slideware. • Engineer the delivery system. Own CI/CD for what you build: build, test, release, environments. • Consult. Present to and defend decisions in front of CTOs, VPs of Engineering, and business leadership. Quantify impact in their terms. Support pre-sales scoping and proposal work when the deal calls for it. What We're Looking For • 5+ years of professional software development experience with at least one mainstream production stack (TypeScript/Node.js, Python, Java, C#/.NET, or Go). • 1 - 4 years of professional experience building and shipping LLM-powered and agentic AI systems beyond prototypes or demos. • Advanced English is required. • Successful completion of a four-year college degree is required. • Hands-on experience with frontier AI developer tools including Cursor, Claude Code, Gemini CLI, or OpenAI Codex. • Proven experience evaluating current-state environments and defining technical roadmaps. • Demonstrated experience with APIs, service design, event-driven and distributed systems, and data modeling. • Experience designing and owning CI/CD pipelines, including build, test, release, environments, and deployment processes. • Strong understanding of context and specification engineering to produce reliable AI output. • Solid understanding of enterprise integration patterns, including APIs, webhooks, event streams, iPaaS platforms such as n8n or MuleSoft, and MCP. • Track record of leading client-facing discovery calls, asking "why" before "how" to decompose unstructured business problems. • Skilled at exercising end-to-end delivery ownership and managing project scope without requiring product manager supervision. • Ability to work through new and difficult issues and contribute to libraries as needed. • Ability to create and maintain continuous integration and delivery of applications. • Forensic attention to detail. • A positive mindset and a can-do attitude. • Experience working on Agile / Scrum teams. • Meaningful experience working on large, complex systems. • Ability to take extreme ownership over your work. Every day is a challenge to ensure you are performing to the expectations you and your team have agreed upon. • Ability to identify with the goals of FullStack's clients, and dedicate yourself to delivering on the commitments you and your team make to them. Strong Plus • Production AI operations (Day 2): eval harnesses, observability and tracing, cost and token economics, drift, debugging non-deterministic systems. • Prompt- and eval-level regression testing running in CI and gating releases. • Delivery in regulated environments (financial services, healthcare, insurance, manufacturing). • Pre-sales support: discovery calls, SOW and estimate shaping, technical proposal defense. • Process mining, operations analysis, or transformation consulting background. • Client enablement and training — building the client team’s capability, not just the system. • Prior consulting or agency environment; comfort with ambiguity and multiple accounts. Work Authorization Requirements • Applicants must be currently authorized to work in the United States on a full-time basis. • FullStack will not sponsor applicants for work visas now or in the future. What We Offer • Competitive Salary. • Paid Time Off (vacation, sick leave, parental leave, holidays). • 100% remote work. • The ability to work with leading startups and Fortune 500 companies. • Health, dental, and vision insurance. • 401(k) w/ 4% match. • Ample opportunity for career advancement. • Continuing education opportunities. FullStack is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. Learn more about our Applicants Privacy Notice.

You would lead client discovery, design and build production AI and retrieval systems, and own their evaluation, deployment, and ongoing operations. | The role requires 5+ years of software development, 1–4 years shipping production agentic AI or RAG solutions, client-facing discovery and architecture leadership, and a four-year degree. | About FullStack FullStack is your AI-native engineering partner, built to turn AI capability into certainty. Most companies can demo AI; few can get it to production and prove what it produced. We close that gap with three capabilities, AI built into your products from the start, elite vetted talent who apply it, and transparent execution that shows value every step of the way. With now over 600 customers in North America, FullStack is one integrated partner for all your AI engineering needs. With FullStack, you move forward with confidence. We’re Most Proud Of • Offering life-changing career opportunities to talented software professionals across the Americas. • Building highly-skilled software development teams for hundreds of the world’s greatest companies. • Having delivered hundreds of successful custom software solutions, which have positively impacted the lives and careers of millions of users. • Our 4.1-star rating on GlassDoor. • Our client Net Promoter Score of 68, twice the industry average. The Position Forward Deployed Engineers sit inside the client’s problem, not next to it. You embed with an enterprise team, find the work that actually warrants AI, architect the system, build it, and stay long enough to make it survive contact with production. This is a three-way role: consultant, operator, engineer. You should be as comfortable pressure-testing a business case with a COO or VP of Engineering as you are diagnosing why retrieval quality collapsed after a data refresh. The people who succeed here can hold a discovery conversation on Monday and ship what they scoped on Thursday. On this track, your edge is data, retrieval, and model behavior: you make agents trustworthy by getting the data, context, and evaluation right. We work with regulated industries and Fortune 500 clients who have AI mandates, real constraints, and low tolerance for demos that don’t hold up. How We Approach The Work FDEs are trusted to shape the solution, not only deliver it. Every engagement calls for architect-level judgment: • Reframes the request. Treats “we need an agent for X” as a hypothesis, not a spec. • Decomposes the problem. Outcome → process → decisions → data → systems → actors → constraints — before choosing technology. • Chooses the intervention. Remove, simplify, traditional software, automation, LLM, RAG, agent, or multi-agent — and defends why. • Owns the architecture decision. Documents trade-offs (ADRs), sets success metrics, pushes back on scope that won’t deliver value. Consultative Discovery Clients rarely arrive with a fully formed problem, and the FDE’s first contribution is helping them see it clearly. You lead discovery with genuine curiosity, moving the conversation from stated requirements to underlying needs, and you build credibility through the depth of your understanding rather than the breadth of your pitch. • Uncovers business drivers. What outcome matters, what it’s worth, why now, and what happens if nothing changes. • Uncovers personal drivers. What each stakeholder is measured on, what they’re worried about, and what a win looks like for them individually. • Surfaces constraints early. Security, compliance, data access, budget, skills, politics, timelines — the things that kill projects in month two. • Aligns stakeholders. Spots conflicting goals between business, engineering, and risk, and brings them to a shared definition of success. • Asks strategic questions. Follows the answer with “why,” “how do you know,” and “what happens today when…” — without interrogating. Clients leave the conversation understanding their own problem better. • Earns authority through depth. Credibility comes from the quality of the questions and the insight in the playback, not from pitching technology. What You'll Do • Discover. Run workshops and process discovery with business, data, and engineering stakeholders. Assess data readiness honestly. Separate genuine AI problems from workflow problems wearing an AI costume. • Structure the process and the spec. For any business process being automated, map the current state, decision points, data inputs, and exceptions, then turn it into specifications that both people and AI systems can execute against reliably. • Architect & design. Design agentic systems end to end with emphasis on context and retrieval architecture — data sourcing, chunking, embeddings, hybrid search, reranking, permissions-aware retrieval, knowledge graphs where warranted — plus orchestration, MCP/tool integration, guardrails, and human-in-the-loop checkpoints. • Build. Ship working systems — agents, retrieval services, evaluation tooling, and the data flows that feed them. Prototypes that prove value in weeks, not slideware. • Engineer the delivery system. Own CI/CD for what you build, extended to AI: prompt and context versioning, eval suites that run in the pipeline and gate releases. • Operate (Day 2). Own what happens after launch — eval harnesses, groundedness and retrieval metrics, observability and tracing, cost and latency management, drift, and the handoff that lets the client’s team run it without you. • Consult. Present to and defend decisions in front of CTOs, VPs of Engineering, CDOs, and business leadership. Quantify impact in their terms. Support pre-sales scoping and proposal work when the deal calls for it. What We're Looking For • 5+ years of professional software development experience with at least one mainstream production stack (Python, TypeScript/Node.js, Java, C#/.NET, or Go). • 1 - 4 years of professional experience shipping agentic AI solutions, LLM workflows, and RAG architectures in production environments. • Advanced English is required. • Successful completion of a four-year college degree is required. • Hands-on experience with frontier AI developer tools including Cursor, Claude Code, Gemini CLI, or OpenAI Codex. • Track record of leading client-facing discovery calls, asking "why" before "how" to decompose unstructured business problems. • Solid understanding of architecture paradigms such as RESTful APIs, microservices, and event-driven systems. • Well-versed in agentic orchestration tools and frameworks like LangGraph, LangChain, N8N, or direct Model Context Protocol (MCP) and LLM APIs. • Skilled at exercising end-to-end delivery ownership and managing project scope without requiring product manager supervision. • Ability to work through new and difficult issues and contribute to libraries as needed. • Ability to create and maintain continuous integration and delivery of applications. • Forensic attention to detail. • A positive mindset and a can-do attitude. • Experience working on Agile / Scrum teams. • Meaningful experience working on large, complex systems. • Ability to take extreme ownership over your work. Every day is a challenge to ensure you are performing to the expectations you and your team have agreed upon. • Ability to identify with the goals of FullStack's clients, and dedicate yourself to delivering on the commitments you and your team make to them. Strong Plus • Production AI operations (Day 2): eval harnesses, observability and tracing, cost and token economics, drift, debugging non-deterministic systems. • Prompt- and eval-level regression testing running in CI and gating releases. • Delivery in regulated environments (financial services, healthcare, insurance, manufacturing). • Pre-sales support: discovery calls, SOW and estimate shaping, technical proposal defense. • Process mining, operations analysis, or transformation consulting background. • Client enablement and training — building the client team’s capability, not just the system. • Prior consulting or agency environment; comfort with ambiguity and multiple accounts. Work Authorization Requirements • Applicants must be currently authorized to work in the country of this job posting on a full-time basis. • FullStack will not sponsor applicants for work visas now or in the future. What We Offer • Competitive Salary. • Paid Time Off (vacation, sick leave, parental leave, holidays). • 100% remote work. • The ability to work with leading startups and Fortune 500 companies. • Health, dental, and vision insurance. • 401(k) w/ 4% match. • Ample opportunity for career advancement. • Continuing education opportunities. FullStack is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. Learn more about our Applicants Privacy Notice.

Lead client discovery, design and build enterprise agentic AI solutions, and own their integrations and delivery pipelines through production. | Requires 5+ years of software development, 1–4 years shipping LLM or agentic systems, a four-year degree, and strength in client discovery, enterprise integration, architecture, and CI/CD. | About FullStack FullStack is your AI-native engineering partner, built to turn AI capability into certainty. Most companies can demo AI; few can get it to production and prove what it produced. We close that gap with three capabilities, AI built into your products from the start, elite vetted talent who apply it, and transparent execution that shows value every step of the way. With now over 600 customers in North America, FullStack is one integrated partner for all your AI engineering needs. With FullStack, you move forward with confidence. We’re Most Proud Of • Offering life-changing career opportunities to talented software professionals across the Americas. • Building highly-skilled software development teams for hundreds of the world’s greatest companies. • Having delivered hundreds of successful custom software solutions, which have positively impacted the lives and careers of millions of users. • Our 4.1-star rating on GlassDoor. • Our client Net Promoter Score of 68, twice the industry average. The Position Forward Deployed Engineers sit inside the client’s problem, not next to it. You embed with an enterprise team, find the work that actually warrants AI, architect the system, build it, and make it hold up in the real environment. This is a three-way role: consultant, operator, engineer. You should be as comfortable pressure-testing a business case with a COO or VP of Engineering as you are debugging why an agent’s tool-calling loop degraded after a context change. The people who succeed here can hold a discovery conversation on Monday and ship what they scoped on Thursday. On this track, your edge is application engineering: you make agents real inside the client’s actual systems, workflows, and delivery pipelines. We work with regulated industries and Fortune 500 clients who have AI mandates, real constraints, and low tolerance for demos that don’t hold up. How We Approach The Work FDEs are trusted to shape the solution, not only deliver it. Every engagement calls for architect-level judgment: • Reframes the request. Treats “we need an agent for X” as a hypothesis, not a spec. • Decomposes the problem. Outcome → process → decisions → data → systems → actors → constraints — before choosing technology. • Chooses the intervention. Remove, simplify, traditional software, automation, LLM, RAG, agent, or multi-agent — and defends why. • Owns the architecture decision. Documents trade-offs (ADRs), sets success metrics, pushes back on scope that won’t deliver value. Consultative Discovery Clients rarely arrive with a fully formed problem, and the FDE’s first contribution is helping them see it clearly. You lead discovery with genuine curiosity, moving the conversation from stated requirements to underlying needs, and you build credibility through the depth of your understanding rather than the breadth of your pitch. • Uncovers business drivers. What outcome matters, what it’s worth, why now, and what happens if nothing changes. • Uncovers personal drivers. What each stakeholder is measured on, what they’re worried about, and what a win looks like for them individually. • Surfaces constraints early. Security, compliance, data access, budget, skills, politics, timelines — the things that kill projects in month two. • Aligns stakeholders. Spots conflicting goals between business, engineering, and risk, and brings them to a shared definition of success. • Asks strategic questions. Follows the answer with “why,” “how do you know,” and “what happens today when…” — without interrogating. Clients leave the conversation understanding their own problem better. • Earns authority through depth. Credibility comes from the quality of the questions and the insight in the playback, not from pitching technology. What You'll Do • Discover. Run workshops and process discovery with business and engineering stakeholders. Separate genuine AI problems from workflow problems wearing an AI costume. Come back with a scoped, defensible point of view. • Structure the process and the spec. For any business or engineering process being automated, map the current state, decision points, and exceptions, then turn it into specifications that both people and AI systems can execute against reliably. • Architect & design. Design agentic systems end to end — orchestration, context and retrieval architecture, tool and MCP integration, guardrails, human-in-the-loop checkpoints, and the boundaries between deterministic and non-deterministic components. • Build. Ship working systems — agents, multi-step automations, internal tooling, integration layers. You are hands-on. Prototypes that prove value in weeks, not slideware. • Engineer the delivery system. Own CI/CD for what you build: build, test, release, environments. • Consult. Present to and defend decisions in front of CTOs, VPs of Engineering, and business leadership. Quantify impact in their terms. Support pre-sales scoping and proposal work when the deal calls for it. What We're Looking For • 5+ years of professional software development experience with at least one mainstream production stack (TypeScript/Node.js, Python, Java, C#/.NET, or Go). • 1 - 4 years of professional experience building and shipping LLM-powered and agentic AI systems beyond prototypes or demos. • Advanced English is required. • Successful completion of a four-year college degree is required. • Hands-on experience with frontier AI developer tools including Cursor, Claude Code, Gemini CLI, or OpenAI Codex. • Proven experience evaluating current-state environments and defining technical roadmaps. • Demonstrated experience with APIs, service design, event-driven and distributed systems, and data modeling. • Experience designing and owning CI/CD pipelines, including build, test, release, environments, and deployment processes. • Strong understanding of context and specification engineering to produce reliable AI output. • Solid understanding of enterprise integration patterns, including APIs, webhooks, event streams, iPaaS platforms such as n8n or MuleSoft, and MCP. • Track record of leading client-facing discovery calls, asking "why" before "how" to decompose unstructured business problems. • Skilled at exercising end-to-end delivery ownership and managing project scope without requiring product manager supervision. • Ability to work through new and difficult issues and contribute to libraries as needed. • Ability to create and maintain continuous integration and delivery of applications. • Forensic attention to detail. • A positive mindset and a can-do attitude. • Experience working on Agile / Scrum teams. • Meaningful experience working on large, complex systems. • Ability to take extreme ownership over your work. Every day is a challenge to ensure you are performing to the expectations you and your team have agreed upon. • Ability to identify with the goals of FullStack's clients, and dedicate yourself to delivering on the commitments you and your team make to them. Strong Plus • Production AI operations (Day 2): eval harnesses, observability and tracing, cost and token economics, drift, debugging non-deterministic systems. • Prompt- and eval-level regression testing running in CI and gating releases. • Delivery in regulated environments (financial services, healthcare, insurance, manufacturing). • Pre-sales support: discovery calls, SOW and estimate shaping, technical proposal defense. • Process mining, operations analysis, or transformation consulting background. • Client enablement and training — building the client team’s capability, not just the system. • Prior consulting or agency environment; comfort with ambiguity and multiple accounts. Work Authorization Requirements • Applicants must be currently authorized to work in the United States on a full-time basis. • FullStack will not sponsor applicants for work visas now or in the future. What We Offer • Competitive Salary. • Paid Time Off (vacation, sick leave, parental leave, holidays). • 100% remote work. • The ability to work with leading startups and Fortune 500 companies. • Health, dental, and vision insurance. • 401(k) w/ 4% match. • Ample opportunity for career advancement. • Continuing education opportunities. FullStack is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. Learn more about our Applicants Privacy Notice.

Discover client workflows and data constraints, architect and build agentic AI and retrieval systems, and own evaluation, deployment, and production operations. | Requires five or more years of software development, one to four years delivering production agentic AI and RAG, a four-year degree, advanced English, and strong client-facing discovery, architecture, and delivery ownership. | About FullStack FullStack is your AI-native engineering partner, built to turn AI capability into certainty. Most companies can demo AI; few can get it to production and prove what it produced. We close that gap with three capabilities, AI built into your products from the start, elite vetted talent who apply it, and transparent execution that shows value every step of the way. With now over 600 customers in North America, FullStack is one integrated partner for all your AI engineering needs. With FullStack, you move forward with confidence. We’re Most Proud Of • Offering life-changing career opportunities to talented software professionals across the Americas. • Building highly-skilled software development teams for hundreds of the world’s greatest companies. • Having delivered hundreds of successful custom software solutions, which have positively impacted the lives and careers of millions of users. • Our 4.1-star rating on GlassDoor. • Our client Net Promoter Score of 68, twice the industry average. The Position Forward Deployed Engineers sit inside the client’s problem, not next to it. You embed with an enterprise team, find the work that actually warrants AI, architect the system, build it, and stay long enough to make it survive contact with production. This is a three-way role: consultant, operator, engineer. You should be as comfortable pressure-testing a business case with a COO or VP of Engineering as you are diagnosing why retrieval quality collapsed after a data refresh. The people who succeed here can hold a discovery conversation on Monday and ship what they scoped on Thursday. On this track, your edge is data, retrieval, and model behavior: you make agents trustworthy by getting the data, context, and evaluation right. We work with regulated industries and Fortune 500 clients who have AI mandates, real constraints, and low tolerance for demos that don’t hold up. How We Approach The Work FDEs are trusted to shape the solution, not only deliver it. Every engagement calls for architect-level judgment: • Reframes the request. Treats “we need an agent for X” as a hypothesis, not a spec. • Decomposes the problem. Outcome → process → decisions → data → systems → actors → constraints — before choosing technology. • Chooses the intervention. Remove, simplify, traditional software, automation, LLM, RAG, agent, or multi-agent — and defends why. • Owns the architecture decision. Documents trade-offs (ADRs), sets success metrics, pushes back on scope that won’t deliver value. Consultative Discovery Clients rarely arrive with a fully formed problem, and the FDE’s first contribution is helping them see it clearly. You lead discovery with genuine curiosity, moving the conversation from stated requirements to underlying needs, and you build credibility through the depth of your understanding rather than the breadth of your pitch. • Uncovers business drivers. What outcome matters, what it’s worth, why now, and what happens if nothing changes. • Uncovers personal drivers. What each stakeholder is measured on, what they’re worried about, and what a win looks like for them individually. • Surfaces constraints early. Security, compliance, data access, budget, skills, politics, timelines — the things that kill projects in month two. • Aligns stakeholders. Spots conflicting goals between business, engineering, and risk, and brings them to a shared definition of success. • Asks strategic questions. Follows the answer with “why,” “how do you know,” and “what happens today when…” — without interrogating. Clients leave the conversation understanding their own problem better. • Earns authority through depth. Credibility comes from the quality of the questions and the insight in the playback, not from pitching technology. What You'll Do • Discover. Run workshops and process discovery with business, data, and engineering stakeholders. Assess data readiness honestly. Separate genuine AI problems from workflow problems wearing an AI costume. • Structure the process and the spec. For any business process being automated, map the current state, decision points, data inputs, and exceptions, then turn it into specifications that both people and AI systems can execute against reliably. • Architect & design. Design agentic systems end to end with emphasis on context and retrieval architecture — data sourcing, chunking, embeddings, hybrid search, reranking, permissions-aware retrieval, knowledge graphs where warranted — plus orchestration, MCP/tool integration, guardrails, and human-in-the-loop checkpoints. • Build. Ship working systems — agents, retrieval services, evaluation tooling, and the data flows that feed them. Prototypes that prove value in weeks, not slideware. • Engineer the delivery system. Own CI/CD for what you build, extended to AI: prompt and context versioning, eval suites that run in the pipeline and gate releases. • Operate (Day 2). Own what happens after launch — eval harnesses, groundedness and retrieval metrics, observability and tracing, cost and latency management, drift, and the handoff that lets the client’s team run it without you. • Consult. Present to and defend decisions in front of CTOs, VPs of Engineering, CDOs, and business leadership. Quantify impact in their terms. Support pre-sales scoping and proposal work when the deal calls for it. What We're Looking For • 5+ years of professional software development experience with at least one mainstream production stack (Python, TypeScript/Node.js, Java, C#/.NET, or Go). • 1 - 4 years of professional experience shipping agentic AI solutions, LLM workflows, and RAG architectures in production environments. • Advanced English is required. • Successful completion of a four-year college degree is required. • Hands-on experience with frontier AI developer tools including Cursor, Claude Code, Gemini CLI, or OpenAI Codex. • Track record of leading client-facing discovery calls, asking "why" before "how" to decompose unstructured business problems. • Solid understanding of architecture paradigms such as RESTful APIs, microservices, and event-driven systems. • Well-versed in agentic orchestration tools and frameworks like LangGraph, LangChain, N8N, or direct Model Context Protocol (MCP) and LLM APIs. • Skilled at exercising end-to-end delivery ownership and managing project scope without requiring product manager supervision. • Ability to work through new and difficult issues and contribute to libraries as needed. • Ability to create and maintain continuous integration and delivery of applications. • Forensic attention to detail. • A positive mindset and a can-do attitude. • Experience working on Agile / Scrum teams. • Meaningful experience working on large, complex systems. • Ability to take extreme ownership over your work. Every day is a challenge to ensure you are performing to the expectations you and your team have agreed upon. • Ability to identify with the goals of FullStack's clients, and dedicate yourself to delivering on the commitments you and your team make to them. Strong Plus • Production AI operations (Day 2): eval harnesses, observability and tracing, cost and token economics, drift, debugging non-deterministic systems. • Prompt- and eval-level regression testing running in CI and gating releases. • Delivery in regulated environments (financial services, healthcare, insurance, manufacturing). • Pre-sales support: discovery calls, SOW and estimate shaping, technical proposal defense. • Process mining, operations analysis, or transformation consulting background. • Client enablement and training — building the client team’s capability, not just the system. • Prior consulting or agency environment; comfort with ambiguity and multiple accounts. Work Authorization Requirements • Applicants must be currently authorized to work in the country of this job posting on a full-time basis. • FullStack will not sponsor applicants for work visas now or in the future. What We Offer • Competitive Salary. • Paid Time Off (vacation, sick leave, parental leave, holidays). • 100% remote work. • The ability to work with leading startups and Fortune 500 companies. • Health, dental, and vision insurance. • 401(k) w/ 4% match. • Ample opportunity for career advancement. • Continuing education opportunities. FullStack is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. Learn more about our Applicants Privacy Notice.

Lead client discovery, architect and build production AI agent systems, and own their delivery pipelines while advising enterprise stakeholders. | Requires 8–10+ years of software engineering, strong backend and architecture experience, production LLM and agent delivery, CI/CD ownership, and proven client-facing consulting skills. | About FullStack FullStack is your AI-native engineering partner, built to turn AI capability into certainty. Most companies can demo AI; few can get it to production and prove what it produced. We close that gap with three capabilities, AI built into your products from the start, elite vetted talent who apply it, and transparent execution that shows value every step of the way. With now over 600 customers in North America, FullStack is one integrated partner for all your AI engineering needs. With FullStack, you move forward with confidence. We’re Most Proud Of • Offering life-changing career opportunities to talented software professionals across the Americas. • Building highly-skilled software development teams for hundreds of the world’s greatest companies. • Having delivered hundreds of successful custom software solutions, which have positively impacted the lives and careers of millions of users. • Our 4.1-star rating on GlassDoor. • Our client Net Promoter Score of 68, twice the industry average. The Position Forward Deployed Engineers sit inside the client’s problem, not next to it. You embed with an enterprise team, find the work that actually warrants AI, architect the system, build it, and make it hold up in the real environment. This is a three-way role: consultant, operator, engineer. You should be as comfortable pressure-testing a business case with a COO or VP of Engineering as you are debugging why an agent’s tool-calling loop degraded after a context change. The people who succeed here can hold a discovery conversation on Monday and ship what they scoped on Thursday. On this track, your edge is application engineering: you make agents real inside the client’s actual systems, workflows, and delivery pipelines. We work with regulated industries and Fortune 500 clients who have AI mandates, real constraints, and low tolerance for demos that don’t hold up. How We Approach The Work FDEs are trusted to shape the solution, not only deliver it. Every engagement calls for architect-level judgment: • Reframes the request. Treats “we need an agent for X” as a hypothesis, not a spec. • Decomposes the problem. Outcome → process → decisions → data → systems → actors → constraints — before choosing technology. • Chooses the intervention. Remove, simplify, traditional software, automation, LLM, RAG, agent, or multi-agent — and defends why. • Owns the architecture decision. Documents trade-offs (ADRs), sets success metrics, pushes back on scope that won’t deliver value. Consultative Discovery Clients rarely arrive with a fully formed problem, and the FDE’s first contribution is helping them see it clearly. You lead discovery with genuine curiosity, moving the conversation from stated requirements to underlying needs, and you build credibility through the depth of your understanding rather than the breadth of your pitch. • Uncovers business drivers. What outcome matters, what it’s worth, why now, and what happens if nothing changes. • Uncovers personal drivers. What each stakeholder is measured on, what they’re worried about, and what a win looks like for them individually. • Surfaces constraints early. Security, compliance, data access, budget, skills, politics, timelines — the things that kill projects in month two. • Aligns stakeholders. Spots conflicting goals between business, engineering, and risk, and brings them to a shared definition of success. • Asks strategic questions. Follows the answer with “why,” “how do you know,” and “what happens today when…” — without interrogating. Clients leave the conversation understanding their own problem better. • Earns authority through depth. Credibility comes from the quality of the questions and the insight in the playback, not from pitching technology. What You'll Do • Discover. Run workshops and process discovery with business and engineering stakeholders. Separate genuine AI problems from workflow problems wearing an AI costume. Come back with a scoped, defensible point of view. • Structure the process and the spec. For any business or engineering process being automated, map the current state, decision points, and exceptions, then turn it into specifications that both people and AI systems can execute against reliably. • Architect & design. Design agentic systems end to end — orchestration, context and retrieval architecture, tool and MCP integration, guardrails, human-in-the-loop checkpoints, and the boundaries between deterministic and non-deterministic components. • Build. Ship working systems — agents, multi-step automations, internal tooling, integration layers. You are hands-on. Prototypes that prove value in weeks, not slideware. • Engineer the delivery system. Own CI/CD for what you build: build, test, release, environments. • Consult. Present to and defend decisions in front of CTOs, VPs of Engineering, and business leadership. Quantify impact in their terms. Support pre-sales scoping and proposal work when the deal calls for it. What We're Looking For Engineering foundation • 8–10+ years of professional software development; back-end-heavy, full-stack capable. • Primary production experience in one of our core ecosystems: TypeScript / Node + React, Python, .NET / C#, or Java. • Strong in APIs, service design, event-driven / distributed systems, and data modeling. • Track record as a Solution Architect, Principal Engineer, or Tech Lead — can assess a current-state environment and define a credible roadmap. • Has designed and owned CI/CD — build, test, release, environments — not just used someone else’s pipeline. Applied AI • Has built and shipped LLM and agent systems beyond a demo. • Deep fluency with agentic tooling: Claude Code, agent frameworks, MCP, tool/function calling, multi-agent orchestration. • Context and spec engineering: structures codebases, specifications, process definitions, and context to produce reliable, context-efficient AI output. • Integration literacy: understands integration methods and options — APIs, webhooks, event streams, iPaaS (n8n, MuleSoft, etc.), MCP — and the constraints and strategies behind connecting AI to systems of record. Doesn’t need to own every integration, but can design around them and guide the team that does. Consulting & business • Demonstrates the discovery behaviors above: business and personal drivers, constraints, stakeholder alignment, strategic questioning. • Runs client conversations without an account manager in the room: workshops, proposal walkthroughs, hard questions, pushback. • Translates technical decisions into business outcomes — cycle time, cost per transaction, throughput, risk reduction, ROI. • Exceptional written and verbal communication; credible in front of senior stakeholders. Strong Plus • Production AI operations (Day 2): eval harnesses, observability and tracing, cost and token economics, drift, debugging non-deterministic systems. • Prompt- and eval-level regression testing running in CI and gating releases. • Delivery in regulated environments (financial services, healthcare, insurance, manufacturing). • Pre-sales support: discovery calls, SOW and estimate shaping, technical proposal defense. • Process mining, operations analysis, or transformation consulting background. • Client enablement and training — building the client team’s capability, not just the system. • Prior consulting or agency environment; comfort with ambiguity and multiple accounts. Work Authorization Requirements • Applicants must be currently authorized to work in the United States on a full-time basis. • FullStack will not sponsor applicants for work visas now or in the future. What We Offer • Competitive Salary. • Paid Time Off (vacation, sick leave, parental leave, holidays). • 100% remote work. • The ability to work with leading startups and Fortune 500 companies. • Health, dental, and vision insurance. • 401(k) w/ 4% match. • Ample opportunity for career advancement. • Continuing education opportunities. FullStack is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. Learn more about our Applicants Privacy Notice.

Design, build, and deploy AI-integrated software systems embedded within client environments, combining engineering, consulting, and operational roles. | 8-10+ years software development with expertise in TypeScript/Node and React, solution architecture experience, and applied AI system development and integration. | About FullStack FullStack is your AI-native engineering partner, built to turn AI capability into certainty. Most companies can demo AI; few can get it to production and prove what it produced. We close that gap with three capabilities, AI built into your products from the start, elite vetted talent who apply it, and transparent execution that shows value every step of the way. With now over 600 customers in North America, FullStack is one integrated partner for all your AI engineering needs. With FullStack, you move forward with confidence. We’re Most Proud Of • Offering life-changing career opportunities to talented software professionals across the Americas. • Building highly-skilled software development teams for hundreds of the world’s greatest companies. • Having delivered hundreds of successful custom software solutions, which have positively impacted the lives and careers of millions of users. • Our 4.1-star rating on GlassDoor. • Our client Net Promoter Score of 68, twice the industry average. The Position Forward Deployed Engineers sit inside the client’s problem, not next to it. You embed with an enterprise team, find the work that actually warrants AI, architect the system, build it, and make it hold up in the real environment. This is a three-way role: consultant, operator, engineer. You should be as comfortable pressure-testing a business case with a COO or VP of Engineering as you are debugging why an agent’s tool-calling loop degraded after a context change. The people who succeed here can hold a discovery conversation on Monday and ship what they scoped on Thursday. On this track, your edge is application engineering: you make agents real inside the client’s actual systems, workflows, and delivery pipelines. We work with regulated industries and Fortune 500 clients who have AI mandates, real constraints, and low tolerance for demos that don’t hold up. How We Approach The Work FDEs are trusted to shape the solution, not only deliver it. Every engagement calls for architect-level judgment: • Reframes the request. Treats “we need an agent for X” as a hypothesis, not a spec. • Decomposes the problem. Outcome → process → decisions → data → systems → actors → constraints — before choosing technology. • Chooses the intervention. Remove, simplify, traditional software, automation, LLM, RAG, agent, or multi-agent — and defends why. • Owns the architecture decision. Documents trade-offs (ADRs), sets success metrics, pushes back on scope that won’t deliver value. Consultative Discovery Clients rarely arrive with a fully formed problem, and the FDE’s first contribution is helping them see it clearly. You lead discovery with genuine curiosity, moving the conversation from stated requirements to underlying needs, and you build credibility through the depth of your understanding rather than the breadth of your pitch. • Uncovers business drivers. What outcome matters, what it’s worth, why now, and what happens if nothing changes. • Uncovers personal drivers. What each stakeholder is measured on, what they’re worried about, and what a win looks like for them individually. • Surfaces constraints early. Security, compliance, data access, budget, skills, politics, timelines — the things that kill projects in month two. • Aligns stakeholders. Spots conflicting goals between business, engineering, and risk, and brings them to a shared definition of success. • Asks strategic questions. Follows the answer with “why,” “how do you know,” and “what happens today when…” — without interrogating. Clients leave the conversation understanding their own problem better. • Earns authority through depth. Credibility comes from the quality of the questions and the insight in the playback, not from pitching technology. What You'll Do • Discover. Run workshops and process discovery with business and engineering stakeholders. Separate genuine AI problems from workflow problems wearing an AI costume. Come back with a scoped, defensible point of view. • Structure the process and the spec. For any business or engineering process being automated, map the current state, decision points, and exceptions, then turn it into specifications that both people and AI systems can execute against reliably. • Architect & design. Design agentic systems end to end — orchestration, context and retrieval architecture, tool and MCP integration, guardrails, human-in-the-loop checkpoints, and the boundaries between deterministic and non-deterministic components. • Build. Ship working systems — agents, multi-step automations, internal tooling, integration layers. You are hands-on. Prototypes that prove value in weeks, not slideware. • Engineer the delivery system. Own CI/CD for what you build: build, test, release, environments. • Consult. Present to and defend decisions in front of CTOs, VPs of Engineering, and business leadership. Quantify impact in their terms. Support pre-sales scoping and proposal work when the deal calls for it. What We're Looking For Engineering foundation • 8–10+ years of professional software development; back-end-heavy, full-stack capable. • Primary production experience in one of our core ecosystems: TypeScript / Node + React, Python, .NET / C#, or Java. • Strong in APIs, service design, event-driven / distributed systems, and data modeling. • Track record as a Solution Architect, Principal Engineer, or Tech Lead — can assess a current-state environment and define a credible roadmap. • Has designed and owned CI/CD — build, test, release, environments — not just used someone else’s pipeline. Applied AI • Has built and shipped LLM and agent systems beyond a demo. • Deep fluency with agentic tooling: Claude Code, agent frameworks, MCP, tool/function calling, multi-agent orchestration. • Context and spec engineering: structures codebases, specifications, process definitions, and context to produce reliable, context-efficient AI output. • Integration literacy: understands integration methods and options — APIs, webhooks, event streams, iPaaS (n8n, MuleSoft, etc.), MCP — and the constraints and strategies behind connecting AI to systems of record. Doesn’t need to own every integration, but can design around them and guide the team that does. Consulting & business • Demonstrates the discovery behaviors above: business and personal drivers, constraints, stakeholder alignment, strategic questioning. • Runs client conversations without an account manager in the room: workshops, proposal walkthroughs, hard questions, pushback. • Translates technical decisions into business outcomes — cycle time, cost per transaction, throughput, risk reduction, ROI. • Exceptional written and verbal communication; credible in front of senior stakeholders. Strong Plus • Production AI operations (Day 2): eval harnesses, observability and tracing, cost and token economics, drift, debugging non-deterministic systems. • Prompt- and eval-level regression testing running in CI and gating releases. • Delivery in regulated environments (financial services, healthcare, insurance, manufacturing). • Pre-sales support: discovery calls, SOW and estimate shaping, technical proposal defense. • Process mining, operations analysis, or transformation consulting background. • Client enablement and training — building the client team’s capability, not just the system. • Prior consulting or agency environment; comfort with ambiguity and multiple accounts. Work Authorization Requirements • Applicants must be currently authorized to work in the United States on a full-time basis. • FullStack will not sponsor applicants for work visas now or in the future. What We Offer • Competitive Salary. • Paid Time Off (vacation, sick leave, parental leave, holidays). • 100% remote work. • The ability to work with leading startups and Fortune 500 companies. • Health, dental, and vision insurance. • 401(k) w/ 4% match. • Ample opportunity for career advancement. • Continuing education opportunities. FullStack is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. Learn more about our Applicants Privacy Notice.

Discover client needs, architect and build production AI agent systems, integrate them with enterprise workflows, and own delivery through release while advising senior stakeholders. | Requires extensive software engineering experience, production LLM and agent development, CI/CD ownership, solution architecture, and strong consulting and stakeholder communication skills. | About FullStack FullStack is your AI-native engineering partner, built to turn AI capability into certainty. Most companies can demo AI; few can get it to production and prove what it produced. We close that gap with three capabilities, AI built into your products from the start, elite vetted talent who apply it, and transparent execution that shows value every step of the way. With now over 600 customers in North America, FullStack is one integrated partner for all your AI engineering needs. With FullStack, you move forward with confidence. We’re Most Proud Of • Offering life-changing career opportunities to talented software professionals across the Americas. • Building highly-skilled software development teams for hundreds of the world’s greatest companies. • Having delivered hundreds of successful custom software solutions, which have positively impacted the lives and careers of millions of users. • Our 4.1-star rating on GlassDoor. • Our client Net Promoter Score of 68, twice the industry average. The Position Forward Deployed Engineers sit inside the client’s problem, not next to it. You embed with an enterprise team, find the work that actually warrants AI, architect the system, build it, and make it hold up in the real environment. This is a three-way role: consultant, operator, engineer. You should be as comfortable pressure-testing a business case with a COO or VP of Engineering as you are debugging why an agent’s tool-calling loop degraded after a context change. The people who succeed here can hold a discovery conversation on Monday and ship what they scoped on Thursday. On this track, your edge is application engineering: you make agents real inside the client’s actual systems, workflows, and delivery pipelines. We work with regulated industries and Fortune 500 clients who have AI mandates, real constraints, and low tolerance for demos that don’t hold up. How We Approach The Work FDEs are trusted to shape the solution, not only deliver it. Every engagement calls for architect-level judgment: • Reframes the request. Treats “we need an agent for X” as a hypothesis, not a spec. • Decomposes the problem. Outcome → process → decisions → data → systems → actors → constraints — before choosing technology. • Chooses the intervention. Remove, simplify, traditional software, automation, LLM, RAG, agent, or multi-agent — and defends why. • Owns the architecture decision. Documents trade-offs (ADRs), sets success metrics, pushes back on scope that won’t deliver value. Consultative Discovery Clients rarely arrive with a fully formed problem, and the FDE’s first contribution is helping them see it clearly. You lead discovery with genuine curiosity, moving the conversation from stated requirements to underlying needs, and you build credibility through the depth of your understanding rather than the breadth of your pitch. • Uncovers business drivers. What outcome matters, what it’s worth, why now, and what happens if nothing changes. • Uncovers personal drivers. What each stakeholder is measured on, what they’re worried about, and what a win looks like for them individually. • Surfaces constraints early. Security, compliance, data access, budget, skills, politics, timelines — the things that kill projects in month two. • Aligns stakeholders. Spots conflicting goals between business, engineering, and risk, and brings them to a shared definition of success. • Asks strategic questions. Follows the answer with “why,” “how do you know,” and “what happens today when…” — without interrogating. Clients leave the conversation understanding their own problem better. • Earns authority through depth. Credibility comes from the quality of the questions and the insight in the playback, not from pitching technology. What You'll Do • Discover. Run workshops and process discovery with business and engineering stakeholders. Separate genuine AI problems from workflow problems wearing an AI costume. Come back with a scoped, defensible point of view. • Structure the process and the spec. For any business or engineering process being automated, map the current state, decision points, and exceptions, then turn it into specifications that both people and AI systems can execute against reliably. • Architect & design. Design agentic systems end to end — orchestration, context and retrieval architecture, tool and MCP integration, guardrails, human-in-the-loop checkpoints, and the boundaries between deterministic and non-deterministic components. • Build. Ship working systems — agents, multi-step automations, internal tooling, integration layers. You are hands-on. Prototypes that prove value in weeks, not slideware. • Engineer the delivery system. Own CI/CD for what you build: build, test, release, environments. • Consult. Present to and defend decisions in front of CTOs, VPs of Engineering, and business leadership. Quantify impact in their terms. Support pre-sales scoping and proposal work when the deal calls for it. What We're Looking For Engineering foundation • 8–10+ years of professional software development; back-end-heavy, full-stack capable. • Primary production experience in one of our core ecosystems: TypeScript / Node + React, Python, .NET / C#, or Java. • Strong in APIs, service design, event-driven / distributed systems, and data modeling. • Track record as a Solution Architect, Principal Engineer, or Tech Lead — can assess a current-state environment and define a credible roadmap. • Has designed and owned CI/CD — build, test, release, environments — not just used someone else’s pipeline. Applied AI • Has built and shipped LLM and agent systems beyond a demo. • Deep fluency with agentic tooling: Claude Code, agent frameworks, MCP, tool/function calling, multi-agent orchestration. • Context and spec engineering: structures codebases, specifications, process definitions, and context to produce reliable, context-efficient AI output. • Integration literacy: understands integration methods and options — APIs, webhooks, event streams, iPaaS (n8n, MuleSoft, etc.), MCP — and the constraints and strategies behind connecting AI to systems of record. Doesn’t need to own every integration, but can design around them and guide the team that does. Consulting & business • Demonstrates the discovery behaviors above: business and personal drivers, constraints, stakeholder alignment, strategic questioning. • Runs client conversations without an account manager in the room: workshops, proposal walkthroughs, hard questions, pushback. • Translates technical decisions into business outcomes — cycle time, cost per transaction, throughput, risk reduction, ROI. • Exceptional written and verbal communication; credible in front of senior stakeholders. Strong Plus • Production AI operations (Day 2): eval harnesses, observability and tracing, cost and token economics, drift, debugging non-deterministic systems. • Prompt- and eval-level regression testing running in CI and gating releases. • Delivery in regulated environments (financial services, healthcare, insurance, manufacturing). • Pre-sales support: discovery calls, SOW and estimate shaping, technical proposal defense. • Process mining, operations analysis, or transformation consulting background. • Client enablement and training — building the client team’s capability, not just the system. • Prior consulting or agency environment; comfort with ambiguity and multiple accounts. Work Authorization Requirements • Applicants must be currently authorized to work in the United States on a full-time basis. • FullStack will not sponsor applicants for work visas now or in the future. What We Offer • Competitive Salary. • Paid Time Off (vacation, sick leave, parental leave, holidays). • 100% remote work. • The ability to work with leading startups and Fortune 500 companies. • Health, dental, and vision insurance. • 401(k) w/ 4% match. • Ample opportunity for career advancement. • Continuing education opportunities. FullStack is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. Learn more about our Applicants Privacy Notice.

Embed with client teams to architect, build, and deploy AI-driven agent systems integrated into enterprise workflows. | Requires 8-10+ years software development, strong backend and full-stack skills, AI/agent system production experience, consulting ability, and senior technical leadership. | About FullStack FullStack is your AI-native engineering partner, built to turn AI capability into certainty. Most companies can demo AI; few can get it to production and prove what it produced. We close that gap with three capabilities, AI built into your products from the start, elite vetted talent who apply it, and transparent execution that shows value every step of the way. With now over 600 customers in North America, FullStack is one integrated partner for all your AI engineering needs. With FullStack, you move forward with confidence. We’re Most Proud Of • Offering life-changing career opportunities to talented software professionals across the Americas. • Building highly-skilled software development teams for hundreds of the world’s greatest companies. • Having delivered hundreds of successful custom software solutions, which have positively impacted the lives and careers of millions of users. • Our 4.1-star rating on GlassDoor. • Our client Net Promoter Score of 68, twice the industry average. The Position Forward Deployed Engineers sit inside the client’s problem, not next to it. You embed with an enterprise team, find the work that actually warrants AI, architect the system, build it, and make it hold up in the real environment. This is a three-way role: consultant, operator, engineer. You should be as comfortable pressure-testing a business case with a COO or VP of Engineering as you are debugging why an agent’s tool-calling loop degraded after a context change. The people who succeed here can hold a discovery conversation on Monday and ship what they scoped on Thursday. On this track, your edge is application engineering: you make agents real inside the client’s actual systems, workflows, and delivery pipelines. We work with regulated industries and Fortune 500 clients who have AI mandates, real constraints, and low tolerance for demos that don’t hold up. How We Approach The Work FDEs are trusted to shape the solution, not only deliver it. Every engagement calls for architect-level judgment: • Reframes the request. Treats “we need an agent for X” as a hypothesis, not a spec. • Decomposes the problem. Outcome → process → decisions → data → systems → actors → constraints — before choosing technology. • Chooses the intervention. Remove, simplify, traditional software, automation, LLM, RAG, agent, or multi-agent — and defends why. • Owns the architecture decision. Documents trade-offs (ADRs), sets success metrics, pushes back on scope that won’t deliver value. Consultative Discovery Clients rarely arrive with a fully formed problem, and the FDE’s first contribution is helping them see it clearly. You lead discovery with genuine curiosity, moving the conversation from stated requirements to underlying needs, and you build credibility through the depth of your understanding rather than the breadth of your pitch. • Uncovers business drivers. What outcome matters, what it’s worth, why now, and what happens if nothing changes. • Uncovers personal drivers. What each stakeholder is measured on, what they’re worried about, and what a win looks like for them individually. • Surfaces constraints early. Security, compliance, data access, budget, skills, politics, timelines — the things that kill projects in month two. • Aligns stakeholders. Spots conflicting goals between business, engineering, and risk, and brings them to a shared definition of success. • Asks strategic questions. Follows the answer with “why,” “how do you know,” and “what happens today when…” — without interrogating. Clients leave the conversation understanding their own problem better. • Earns authority through depth. Credibility comes from the quality of the questions and the insight in the playback, not from pitching technology. What You'll Do • Discover. Run workshops and process discovery with business and engineering stakeholders. Separate genuine AI problems from workflow problems wearing an AI costume. Come back with a scoped, defensible point of view. • Structure the process and the spec. For any business or engineering process being automated, map the current state, decision points, and exceptions, then turn it into specifications that both people and AI systems can execute against reliably. • Architect & design. Design agentic systems end to end — orchestration, context and retrieval architecture, tool and MCP integration, guardrails, human-in-the-loop checkpoints, and the boundaries between deterministic and non-deterministic components. • Build. Ship working systems — agents, multi-step automations, internal tooling, integration layers. You are hands-on. Prototypes that prove value in weeks, not slideware. • Engineer the delivery system. Own CI/CD for what you build: build, test, release, environments. • Consult. Present to and defend decisions in front of CTOs, VPs of Engineering, and business leadership. Quantify impact in their terms. Support pre-sales scoping and proposal work when the deal calls for it. What We're Looking For Engineering foundation • 8–10+ years of professional software development; back-end-heavy, full-stack capable. • Primary production experience in one of our core ecosystems: TypeScript / Node + React, Python, .NET / C#, or Java. • Strong in APIs, service design, event-driven / distributed systems, and data modeling. • Track record as a Solution Architect, Principal Engineer, or Tech Lead — can assess a current-state environment and define a credible roadmap. • Has designed and owned CI/CD — build, test, release, environments — not just used someone else’s pipeline. Applied AI • Has built and shipped LLM and agent systems beyond a demo. • Deep fluency with agentic tooling: Claude Code, agent frameworks, MCP, tool/function calling, multi-agent orchestration. • Context and spec engineering: structures codebases, specifications, process definitions, and context to produce reliable, context-efficient AI output. • Integration literacy: understands integration methods and options — APIs, webhooks, event streams, iPaaS (n8n, MuleSoft, etc.), MCP — and the constraints and strategies behind connecting AI to systems of record. Doesn’t need to own every integration, but can design around them and guide the team that does. Consulting & business • Demonstrates the discovery behaviors above: business and personal drivers, constraints, stakeholder alignment, strategic questioning. • Runs client conversations without an account manager in the room: workshops, proposal walkthroughs, hard questions, pushback. • Translates technical decisions into business outcomes — cycle time, cost per transaction, throughput, risk reduction, ROI. • Exceptional written and verbal communication; credible in front of senior stakeholders. Strong Plus • Production AI operations (Day 2): eval harnesses, observability and tracing, cost and token economics, drift, debugging non-deterministic systems. • Prompt- and eval-level regression testing running in CI and gating releases. • Delivery in regulated environments (financial services, healthcare, insurance, manufacturing). • Pre-sales support: discovery calls, SOW and estimate shaping, technical proposal defense. • Process mining, operations analysis, or transformation consulting background. • Client enablement and training — building the client team’s capability, not just the system. • Prior consulting or agency environment; comfort with ambiguity and multiple accounts. Work Authorization Requirements • Applicants must be currently authorized to work in the United States on a full-time basis. • FullStack will not sponsor applicants for work visas now or in the future. What We Offer • Competitive Salary. • Paid Time Off (vacation, sick leave, parental leave, holidays). • 100% remote work. • The ability to work with leading startups and Fortune 500 companies. • Health, dental, and vision insurance. • 401(k) w/ 4% match. • Ample opportunity for career advancement. • Continuing education opportunities. FullStack is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. Learn more about our Applicants Privacy Notice.

Embed with client teams to architect, build, and deliver AI-powered systems integrated into real environments. | 8-10+ years software development with strong TypeScript and backend skills, AI/agent system experience, solution architecture, consulting, and communication abilities. | About FullStack FullStack is your AI-native engineering partner, built to turn AI capability into certainty. Most companies can demo AI; few can get it to production and prove what it produced. We close that gap with three capabilities, AI built into your products from the start, elite vetted talent who apply it, and transparent execution that shows value every step of the way. With now over 600 customers in North America, FullStack is one integrated partner for all your AI engineering needs. With FullStack, you move forward with confidence. We’re Most Proud Of • Offering life-changing career opportunities to talented software professionals across the Americas. • Building highly-skilled software development teams for hundreds of the world’s greatest companies. • Having delivered hundreds of successful custom software solutions, which have positively impacted the lives and careers of millions of users. • Our 4.1-star rating on GlassDoor. • Our client Net Promoter Score of 68, twice the industry average. The Position Forward Deployed Engineers sit inside the client’s problem, not next to it. You embed with an enterprise team, find the work that actually warrants AI, architect the system, build it, and make it hold up in the real environment. This is a three-way role: consultant, operator, engineer. You should be as comfortable pressure-testing a business case with a COO or VP of Engineering as you are debugging why an agent’s tool-calling loop degraded after a context change. The people who succeed here can hold a discovery conversation on Monday and ship what they scoped on Thursday. On this track, your edge is application engineering: you make agents real inside the client’s actual systems, workflows, and delivery pipelines. We work with regulated industries and Fortune 500 clients who have AI mandates, real constraints, and low tolerance for demos that don’t hold up. How We Approach The Work FDEs are trusted to shape the solution, not only deliver it. Every engagement calls for architect-level judgment: • Reframes the request. Treats “we need an agent for X” as a hypothesis, not a spec. • Decomposes the problem. Outcome → process → decisions → data → systems → actors → constraints — before choosing technology. • Chooses the intervention. Remove, simplify, traditional software, automation, LLM, RAG, agent, or multi-agent — and defends why. • Owns the architecture decision. Documents trade-offs (ADRs), sets success metrics, pushes back on scope that won’t deliver value. Consultative Discovery Clients rarely arrive with a fully formed problem, and the FDE’s first contribution is helping them see it clearly. You lead discovery with genuine curiosity, moving the conversation from stated requirements to underlying needs, and you build credibility through the depth of your understanding rather than the breadth of your pitch. • Uncovers business drivers. What outcome matters, what it’s worth, why now, and what happens if nothing changes. • Uncovers personal drivers. What each stakeholder is measured on, what they’re worried about, and what a win looks like for them individually. • Surfaces constraints early. Security, compliance, data access, budget, skills, politics, timelines — the things that kill projects in month two. • Aligns stakeholders. Spots conflicting goals between business, engineering, and risk, and brings them to a shared definition of success. • Asks strategic questions. Follows the answer with “why,” “how do you know,” and “what happens today when…” — without interrogating. Clients leave the conversation understanding their own problem better. • Earns authority through depth. Credibility comes from the quality of the questions and the insight in the playback, not from pitching technology. What You'll Do • Discover. Run workshops and process discovery with business and engineering stakeholders. Separate genuine AI problems from workflow problems wearing an AI costume. Come back with a scoped, defensible point of view. • Structure the process and the spec. For any business or engineering process being automated, map the current state, decision points, and exceptions, then turn it into specifications that both people and AI systems can execute against reliably. • Architect & design. Design agentic systems end to end — orchestration, context and retrieval architecture, tool and MCP integration, guardrails, human-in-the-loop checkpoints, and the boundaries between deterministic and non-deterministic components. • Build. Ship working systems — agents, multi-step automations, internal tooling, integration layers. You are hands-on. Prototypes that prove value in weeks, not slideware. • Engineer the delivery system. Own CI/CD for what you build: build, test, release, environments. • Consult. Present to and defend decisions in front of CTOs, VPs of Engineering, and business leadership. Quantify impact in their terms. Support pre-sales scoping and proposal work when the deal calls for it. What We're Looking For Engineering foundation • 8–10+ years of professional software development; back-end-heavy, full-stack capable. • Primary production experience in one of our core ecosystems: TypeScript / Node + React, Python, .NET / C#, or Java. • Strong in APIs, service design, event-driven / distributed systems, and data modeling. • Track record as a Solution Architect, Principal Engineer, or Tech Lead — can assess a current-state environment and define a credible roadmap. • Has designed and owned CI/CD — build, test, release, environments — not just used someone else’s pipeline. Applied AI • Has built and shipped LLM and agent systems beyond a demo. • Deep fluency with agentic tooling: Claude Code, agent frameworks, MCP, tool/function calling, multi-agent orchestration. • Context and spec engineering: structures codebases, specifications, process definitions, and context to produce reliable, context-efficient AI output. • Integration literacy: understands integration methods and options — APIs, webhooks, event streams, iPaaS (n8n, MuleSoft, etc.), MCP — and the constraints and strategies behind connecting AI to systems of record. Doesn’t need to own every integration, but can design around them and guide the team that does. Consulting & business • Demonstrates the discovery behaviors above: business and personal drivers, constraints, stakeholder alignment, strategic questioning. • Runs client conversations without an account manager in the room: workshops, proposal walkthroughs, hard questions, pushback. • Translates technical decisions into business outcomes — cycle time, cost per transaction, throughput, risk reduction, ROI. • Exceptional written and verbal communication; credible in front of senior stakeholders. Strong Plus • Production AI operations (Day 2): eval harnesses, observability and tracing, cost and token economics, drift, debugging non-deterministic systems. • Prompt- and eval-level regression testing running in CI and gating releases. • Delivery in regulated environments (financial services, healthcare, insurance, manufacturing). • Pre-sales support: discovery calls, SOW and estimate shaping, technical proposal defense. • Process mining, operations analysis, or transformation consulting background. • Client enablement and training — building the client team’s capability, not just the system. • Prior consulting or agency environment; comfort with ambiguity and multiple accounts. Work Authorization Requirements • Applicants must be currently authorized to work in the United States on a full-time basis. • FullStack will not sponsor applicants for work visas now or in the future. What We Offer • Competitive Salary. • Paid Time Off (vacation, sick leave, parental leave, holidays). • 100% remote work. • The ability to work with leading startups and Fortune 500 companies. • Health, dental, and vision insurance. • 401(k) w/ 4% match. • Ample opportunity for career advancement. • Continuing education opportunities. FullStack is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. Learn more about our Applicants Privacy Notice.

Architect, build, and deploy AI agent systems embedded within client workflows while consulting with business and engineering stakeholders. | 8-10+ years software development, strong TypeScript/Node/React skills, solution architecture experience, and applied AI/agent system production expertise. | About FullStack FullStack is your AI-native engineering partner, built to turn AI capability into certainty. Most companies can demo AI; few can get it to production and prove what it produced. We close that gap with three capabilities, AI built into your products from the start, elite vetted talent who apply it, and transparent execution that shows value every step of the way. With now over 600 customers in North America, FullStack is one integrated partner for all your AI engineering needs. With FullStack, you move forward with confidence. We’re Most Proud Of • Offering life-changing career opportunities to talented software professionals across the Americas. • Building highly-skilled software development teams for hundreds of the world’s greatest companies. • Having delivered hundreds of successful custom software solutions, which have positively impacted the lives and careers of millions of users. • Our 4.1-star rating on GlassDoor. • Our client Net Promoter Score of 68, twice the industry average. The Position Forward Deployed Engineers sit inside the client’s problem, not next to it. You embed with an enterprise team, find the work that actually warrants AI, architect the system, build it, and make it hold up in the real environment. This is a three-way role: consultant, operator, engineer. You should be as comfortable pressure-testing a business case with a COO or VP of Engineering as you are debugging why an agent’s tool-calling loop degraded after a context change. The people who succeed here can hold a discovery conversation on Monday and ship what they scoped on Thursday. On this track, your edge is application engineering: you make agents real inside the client’s actual systems, workflows, and delivery pipelines. We work with regulated industries and Fortune 500 clients who have AI mandates, real constraints, and low tolerance for demos that don’t hold up. How We Approach The Work FDEs are trusted to shape the solution, not only deliver it. Every engagement calls for architect-level judgment: • Reframes the request. Treats “we need an agent for X” as a hypothesis, not a spec. • Decomposes the problem. Outcome → process → decisions → data → systems → actors → constraints — before choosing technology. • Chooses the intervention. Remove, simplify, traditional software, automation, LLM, RAG, agent, or multi-agent — and defends why. • Owns the architecture decision. Documents trade-offs (ADRs), sets success metrics, pushes back on scope that won’t deliver value. Consultative Discovery Clients rarely arrive with a fully formed problem, and the FDE’s first contribution is helping them see it clearly. You lead discovery with genuine curiosity, moving the conversation from stated requirements to underlying needs, and you build credibility through the depth of your understanding rather than the breadth of your pitch. • Uncovers business drivers. What outcome matters, what it’s worth, why now, and what happens if nothing changes. • Uncovers personal drivers. What each stakeholder is measured on, what they’re worried about, and what a win looks like for them individually. • Surfaces constraints early. Security, compliance, data access, budget, skills, politics, timelines — the things that kill projects in month two. • Aligns stakeholders. Spots conflicting goals between business, engineering, and risk, and brings them to a shared definition of success. • Asks strategic questions. Follows the answer with “why,” “how do you know,” and “what happens today when…” — without interrogating. Clients leave the conversation understanding their own problem better. • Earns authority through depth. Credibility comes from the quality of the questions and the insight in the playback, not from pitching technology. What You'll Do • Discover. Run workshops and process discovery with business and engineering stakeholders. Separate genuine AI problems from workflow problems wearing an AI costume. Come back with a scoped, defensible point of view. • Structure the process and the spec. For any business or engineering process being automated, map the current state, decision points, and exceptions, then turn it into specifications that both people and AI systems can execute against reliably. • Architect & design. Design agentic systems end to end — orchestration, context and retrieval architecture, tool and MCP integration, guardrails, human-in-the-loop checkpoints, and the boundaries between deterministic and non-deterministic components. • Build. Ship working systems — agents, multi-step automations, internal tooling, integration layers. You are hands-on. Prototypes that prove value in weeks, not slideware. • Engineer the delivery system. Own CI/CD for what you build: build, test, release, environments. • Consult. Present to and defend decisions in front of CTOs, VPs of Engineering, and business leadership. Quantify impact in their terms. Support pre-sales scoping and proposal work when the deal calls for it. What We're Looking For Engineering foundation • 8–10+ years of professional software development; back-end-heavy, full-stack capable. • Primary production experience in one of our core ecosystems: TypeScript / Node + React, Python, .NET / C#, or Java. • Strong in APIs, service design, event-driven / distributed systems, and data modeling. • Track record as a Solution Architect, Principal Engineer, or Tech Lead — can assess a current-state environment and define a credible roadmap. • Has designed and owned CI/CD — build, test, release, environments — not just used someone else’s pipeline. Applied AI • Has built and shipped LLM and agent systems beyond a demo. • Deep fluency with agentic tooling: Claude Code, agent frameworks, MCP, tool/function calling, multi-agent orchestration. • Context and spec engineering: structures codebases, specifications, process definitions, and context to produce reliable, context-efficient AI output. • Integration literacy: understands integration methods and options — APIs, webhooks, event streams, iPaaS (n8n, MuleSoft, etc.), MCP — and the constraints and strategies behind connecting AI to systems of record. Doesn’t need to own every integration, but can design around them and guide the team that does. Consulting & business • Demonstrates the discovery behaviors above: business and personal drivers, constraints, stakeholder alignment, strategic questioning. • Runs client conversations without an account manager in the room: workshops, proposal walkthroughs, hard questions, pushback. • Translates technical decisions into business outcomes — cycle time, cost per transaction, throughput, risk reduction, ROI. • Exceptional written and verbal communication; credible in front of senior stakeholders. Strong Plus • Production AI operations (Day 2): eval harnesses, observability and tracing, cost and token economics, drift, debugging non-deterministic systems. • Prompt- and eval-level regression testing running in CI and gating releases. • Delivery in regulated environments (financial services, healthcare, insurance, manufacturing). • Pre-sales support: discovery calls, SOW and estimate shaping, technical proposal defense. • Process mining, operations analysis, or transformation consulting background. • Client enablement and training — building the client team’s capability, not just the system. • Prior consulting or agency environment; comfort with ambiguity and multiple accounts. Work Authorization Requirements • Applicants must be currently authorized to work in the United States on a full-time basis. • FullStack will not sponsor applicants for work visas now or in the future. What We Offer • Competitive Salary. • Paid Time Off (vacation, sick leave, parental leave, holidays). • 100% remote work. • The ability to work with leading startups and Fortune 500 companies. • Health, dental, and vision insurance. • 401(k) w/ 4% match. • Ample opportunity for career advancement. • Continuing education opportunities. FullStack is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. Learn more about our Applicants Privacy Notice.

Design, build, and operate AI-driven systems embedded with client teams, focusing on data, retrieval, and model behavior to deliver production-grade AI solutions. | Requires 8-10+ years in ML, data, and software engineering with Python-first production experience, AI system deployment, consulting skills, and ability to work remotely with Fortune 500 clients. | About FullStack FullStack is your AI-native engineering partner, built to turn AI capability into certainty. Most companies can demo AI; few can get it to production and prove what it produced. We close that gap with three capabilities, AI built into your products from the start, elite vetted talent who apply it, and transparent execution that shows value every step of the way. With now over 600 customers in North America, FullStack is one integrated partner for all your AI engineering needs. With FullStack, you move forward with confidence. We’re Most Proud Of • Offering life-changing career opportunities to talented software professionals across the Americas. • Building highly-skilled software development teams for hundreds of the world’s greatest companies. • Having delivered hundreds of successful custom software solutions, which have positively impacted the lives and careers of millions of users. • Our 4.1-star rating on GlassDoor. • Our client Net Promoter Score of 68, twice the industry average. The Position Forward Deployed Engineers sit inside the client’s problem, not next to it. You embed with an enterprise team, find the work that actually warrants AI, architect the system, build it, and stay long enough to make it survive contact with production. This is a three-way role: consultant, operator, engineer. You should be as comfortable pressure-testing a business case with a COO or VP of Engineering as you are diagnosing why retrieval quality collapsed after a data refresh. The people who succeed here can hold a discovery conversation on Monday and ship what they scoped on Thursday. On this track, your edge is data, retrieval, and model behavior: you make agents trustworthy by getting the data, context, and evaluation right. We work with regulated industries and Fortune 500 clients who have AI mandates, real constraints, and low tolerance for demos that don’t hold up. How We Approach The Work FDEs are trusted to shape the solution, not only deliver it. Every engagement calls for architect-level judgment: • Reframes the request. Treats “we need an agent for X” as a hypothesis, not a spec. • Decomposes the problem. Outcome → process → decisions → data → systems → actors → constraints — before choosing technology. • Chooses the intervention. Remove, simplify, traditional software, automation, LLM, RAG, agent, or multi-agent — and defends why. • Owns the architecture decision. Documents trade-offs (ADRs), sets success metrics, pushes back on scope that won’t deliver value. Consultative Discovery Clients rarely arrive with a fully formed problem, and the FDE’s first contribution is helping them see it clearly. You lead discovery with genuine curiosity, moving the conversation from stated requirements to underlying needs, and you build credibility through the depth of your understanding rather than the breadth of your pitch. • Uncovers business drivers. What outcome matters, what it’s worth, why now, and what happens if nothing changes. • Uncovers personal drivers. What each stakeholder is measured on, what they’re worried about, and what a win looks like for them individually. • Surfaces constraints early. Security, compliance, data access, budget, skills, politics, timelines — the things that kill projects in month two. • Aligns stakeholders. Spots conflicting goals between business, engineering, and risk, and brings them to a shared definition of success. • Asks strategic questions. Follows the answer with “why,” “how do you know,” and “what happens today when…” — without interrogating. Clients leave the conversation understanding their own problem better. • Earns authority through depth. Credibility comes from the quality of the questions and the insight in the playback, not from pitching technology. What You'll Do • Discover. Run workshops and process discovery with business, data, and engineering stakeholders. Assess data readiness honestly. Separate genuine AI problems from workflow problems wearing an AI costume. • Structure the process and the spec. For any business process being automated, map the current state, decision points, data inputs, and exceptions, then turn it into specifications that both people and AI systems can execute against reliably. • Architect & design. Design agentic systems end to end with emphasis on context and retrieval architecture — data sourcing, chunking, embeddings, hybrid search, reranking, permissions-aware retrieval, knowledge graphs where warranted — plus orchestration, MCP/tool integration, guardrails, and human-in-the-loop checkpoints. • Build. Ship working systems — agents, retrieval services, evaluation tooling, and the data flows that feed them. Prototypes that prove value in weeks, not slideware. • Engineer the delivery system. Own CI/CD for what you build, extended to AI: prompt and context versioning, eval suites that run in the pipeline and gate releases. • Operate (Day 2). Own what happens after launch — eval harnesses, groundedness and retrieval metrics, observability and tracing, cost and latency management, drift, and the handoff that lets the client’s team run it without you. • Consult. Present to and defend decisions in front of CTOs, VPs of Engineering, CDOs, and business leadership. Quantify impact in their terms. Support pre-sales scoping and proposal work when the deal calls for it. What We're Looking For Engineering foundation • 8–10+ years across ML, data, and software engineering. • Python-first, production-grade (typed, tested, deployed services — not notebooks only); working proficiency in TypeScript / Node, .NET / C#, or Java is a plus. • Track record as an ML/Data Architect, Principal Engineer, or Tech Lead — can assess a data and AI current state and define a credible roadmap. • Has designed and owned CI/CD for AI or ML systems (LLMOps / MLOps), not just application code handed off to others. Applied / operational AI • Has put LLM and agent systems into production and kept them running — not just proofs of concept or research models. • Deep fluency with retrieval and model behavior: embeddings, vector and hybrid search, reranking, structured output, model selection and routing; sound judgment on fine-tuning vs. prompting vs. retrieval. • Fluent with agentic tooling: Claude Code, agent frameworks, MCP, tool/function calling, multi-agent orchestration. • Context and spec engineering: designs what the model sees — retrieval, context construction, specifications, process definitions, and memory — for reliable, context-efficient output. • Day 2 experience: evaluation design (golden sets, LLM-as-judge, groundedness), observability, cost and token economics, failure-mode analysis. • Data and integration literacy: familiar with ETL/ELT patterns, warehouses/lakehouses (Snowflake, Databricks, BigQuery), and integration options (APIs, event streams, iPaaS, MCP) — enough to design around them, assess data readiness, and guide the data team. Does not need to be an ETL/ELT specialist. Consulting & business • Demonstrates the discovery behaviors above: business and personal drivers, constraints, stakeholder alignment, strategic questioning. • Runs client conversations without an account manager in the room: workshops, proposal walkthroughs, hard questions, pushback. • Translates technical decisions into business outcomes — accuracy, cycle time, cost per transaction, risk reduction, ROI. • Exceptional written and verbal communication; credible in front of senior stakeholders. Strong Plus • Delivery in regulated environments (financial services, healthcare, insurance, manufacturing); data governance and privacy experience. • Pre-sales support: discovery calls, SOW and estimate shaping, technical proposal defense. • Classical ML in production (forecasting, classification, anomaly detection) alongside LLM work. • Client enablement and training — building the client team’s capability, not just the system. • Prior consulting or agency environment; comfort with ambiguity and multiple accounts. Work Authorization Requirements • Applicants must be currently authorized to work in the country of this job posting on a full-time basis. • FullStack will not sponsor applicants for work visas now or in the future. What We Offer • Competitive Salary. • Paid Time Off (vacation, sick leave, parental leave, holidays). • 100% remote work. • The ability to work with leading startups and Fortune 500 companies. • Health, dental, and vision insurance. • 401(k) w/ 4% match. • Ample opportunity for career advancement. • Continuing education opportunities. FullStack is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. Learn more about our Applicants Privacy Notice.

Embed with client teams to architect, build, and deliver AI-powered systems integrating with enterprise workflows. | 8-10+ years software development with strong TypeScript/Node/React skills, solution architect experience, and applied AI system delivery in production. | About FullStack FullStack is your AI-native engineering partner, built to turn AI capability into certainty. Most companies can demo AI; few can get it to production and prove what it produced. We close that gap with three capabilities, AI built into your products from the start, elite vetted talent who apply it, and transparent execution that shows value every step of the way. With now over 600 customers in North America, FullStack is one integrated partner for all your AI engineering needs. With FullStack, you move forward with confidence. We’re Most Proud Of • Offering life-changing career opportunities to talented software professionals across the Americas. • Building highly-skilled software development teams for hundreds of the world’s greatest companies. • Having delivered hundreds of successful custom software solutions, which have positively impacted the lives and careers of millions of users. • Our 4.1-star rating on GlassDoor. • Our client Net Promoter Score of 68, twice the industry average. The Position Forward Deployed Engineers sit inside the client’s problem, not next to it. You embed with an enterprise team, find the work that actually warrants AI, architect the system, build it, and make it hold up in the real environment. This is a three-way role: consultant, operator, engineer. You should be as comfortable pressure-testing a business case with a COO or VP of Engineering as you are debugging why an agent’s tool-calling loop degraded after a context change. The people who succeed here can hold a discovery conversation on Monday and ship what they scoped on Thursday. On this track, your edge is application engineering: you make agents real inside the client’s actual systems, workflows, and delivery pipelines. We work with regulated industries and Fortune 500 clients who have AI mandates, real constraints, and low tolerance for demos that don’t hold up. How We Approach The Work FDEs are trusted to shape the solution, not only deliver it. Every engagement calls for architect-level judgment: • Reframes the request. Treats “we need an agent for X” as a hypothesis, not a spec. • Decomposes the problem. Outcome → process → decisions → data → systems → actors → constraints — before choosing technology. • Chooses the intervention. Remove, simplify, traditional software, automation, LLM, RAG, agent, or multi-agent — and defends why. • Owns the architecture decision. Documents trade-offs (ADRs), sets success metrics, pushes back on scope that won’t deliver value. Consultative Discovery Clients rarely arrive with a fully formed problem, and the FDE’s first contribution is helping them see it clearly. You lead discovery with genuine curiosity, moving the conversation from stated requirements to underlying needs, and you build credibility through the depth of your understanding rather than the breadth of your pitch. • Uncovers business drivers. What outcome matters, what it’s worth, why now, and what happens if nothing changes. • Uncovers personal drivers. What each stakeholder is measured on, what they’re worried about, and what a win looks like for them individually. • Surfaces constraints early. Security, compliance, data access, budget, skills, politics, timelines — the things that kill projects in month two. • Aligns stakeholders. Spots conflicting goals between business, engineering, and risk, and brings them to a shared definition of success. • Asks strategic questions. Follows the answer with “why,” “how do you know,” and “what happens today when…” — without interrogating. Clients leave the conversation understanding their own problem better. • Earns authority through depth. Credibility comes from the quality of the questions and the insight in the playback, not from pitching technology. What You'll Do • Discover. Run workshops and process discovery with business and engineering stakeholders. Separate genuine AI problems from workflow problems wearing an AI costume. Come back with a scoped, defensible point of view. • Structure the process and the spec. For any business or engineering process being automated, map the current state, decision points, and exceptions, then turn it into specifications that both people and AI systems can execute against reliably. • Architect & design. Design agentic systems end to end — orchestration, context and retrieval architecture, tool and MCP integration, guardrails, human-in-the-loop checkpoints, and the boundaries between deterministic and non-deterministic components. • Build. Ship working systems — agents, multi-step automations, internal tooling, integration layers. You are hands-on. Prototypes that prove value in weeks, not slideware. • Engineer the delivery system. Own CI/CD for what you build: build, test, release, environments. • Consult. Present to and defend decisions in front of CTOs, VPs of Engineering, and business leadership. Quantify impact in their terms. Support pre-sales scoping and proposal work when the deal calls for it. What We're Looking For Engineering foundation • 8–10+ years of professional software development; back-end-heavy, full-stack capable. • Primary production experience in one of our core ecosystems: TypeScript / Node + React, Python, .NET / C#, or Java. • Strong in APIs, service design, event-driven / distributed systems, and data modeling. • Track record as a Solution Architect, Principal Engineer, or Tech Lead — can assess a current-state environment and define a credible roadmap. • Has designed and owned CI/CD — build, test, release, environments — not just used someone else’s pipeline. Applied AI • Has built and shipped LLM and agent systems beyond a demo. • Deep fluency with agentic tooling: Claude Code, agent frameworks, MCP, tool/function calling, multi-agent orchestration. • Context and spec engineering: structures codebases, specifications, process definitions, and context to produce reliable, context-efficient AI output. • Integration literacy: understands integration methods and options — APIs, webhooks, event streams, iPaaS (n8n, MuleSoft, etc.), MCP — and the constraints and strategies behind connecting AI to systems of record. Doesn’t need to own every integration, but can design around them and guide the team that does. Consulting & business • Demonstrates the discovery behaviors above: business and personal drivers, constraints, stakeholder alignment, strategic questioning. • Runs client conversations without an account manager in the room: workshops, proposal walkthroughs, hard questions, pushback. • Translates technical decisions into business outcomes — cycle time, cost per transaction, throughput, risk reduction, ROI. • Exceptional written and verbal communication; credible in front of senior stakeholders. Strong Plus • Production AI operations (Day 2): eval harnesses, observability and tracing, cost and token economics, drift, debugging non-deterministic systems. • Prompt- and eval-level regression testing running in CI and gating releases. • Delivery in regulated environments (financial services, healthcare, insurance, manufacturing). • Pre-sales support: discovery calls, SOW and estimate shaping, technical proposal defense. • Process mining, operations analysis, or transformation consulting background. • Client enablement and training — building the client team’s capability, not just the system. • Prior consulting or agency environment; comfort with ambiguity and multiple accounts. Work Authorization Requirements • Applicants must be currently authorized to work in the United States on a full-time basis. • FullStack will not sponsor applicants for work visas now or in the future. What We Offer • Competitive Salary. • Paid Time Off (vacation, sick leave, parental leave, holidays). • 100% remote work. • The ability to work with leading startups and Fortune 500 companies. • Health, dental, and vision insurance. • 401(k) w/ 4% match. • Ample opportunity for career advancement. • Continuing education opportunities. FullStack is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. Learn more about our Applicants Privacy Notice.

Embed with client teams to architect, build, and deploy AI-driven systems integrating with existing workflows and infrastructure. | 8-10+ years software development with strong TypeScript/Node/React skills, experience in APIs and distributed systems, solution architecture experience, and applied AI production experience. | About FullStack FullStack is your AI-native engineering partner, built to turn AI capability into certainty. Most companies can demo AI; few can get it to production and prove what it produced. We close that gap with three capabilities, AI built into your products from the start, elite vetted talent who apply it, and transparent execution that shows value every step of the way. With now over 600 customers in North America, FullStack is one integrated partner for all your AI engineering needs. With FullStack, you move forward with confidence. We’re Most Proud Of • Offering life-changing career opportunities to talented software professionals across the Americas. • Building highly-skilled software development teams for hundreds of the world’s greatest companies. • Having delivered hundreds of successful custom software solutions, which have positively impacted the lives and careers of millions of users. • Our 4.1-star rating on GlassDoor. • Our client Net Promoter Score of 68, twice the industry average. The Position Forward Deployed Engineers sit inside the client’s problem, not next to it. You embed with an enterprise team, find the work that actually warrants AI, architect the system, build it, and make it hold up in the real environment. This is a three-way role: consultant, operator, engineer. You should be as comfortable pressure-testing a business case with a COO or VP of Engineering as you are debugging why an agent’s tool-calling loop degraded after a context change. The people who succeed here can hold a discovery conversation on Monday and ship what they scoped on Thursday. On this track, your edge is application engineering: you make agents real inside the client’s actual systems, workflows, and delivery pipelines. We work with regulated industries and Fortune 500 clients who have AI mandates, real constraints, and low tolerance for demos that don’t hold up. How We Approach The Work FDEs are trusted to shape the solution, not only deliver it. Every engagement calls for architect-level judgment: • Reframes the request. Treats “we need an agent for X” as a hypothesis, not a spec. • Decomposes the problem. Outcome → process → decisions → data → systems → actors → constraints — before choosing technology. • Chooses the intervention. Remove, simplify, traditional software, automation, LLM, RAG, agent, or multi-agent — and defends why. • Owns the architecture decision. Documents trade-offs (ADRs), sets success metrics, pushes back on scope that won’t deliver value. Consultative Discovery Clients rarely arrive with a fully formed problem, and the FDE’s first contribution is helping them see it clearly. You lead discovery with genuine curiosity, moving the conversation from stated requirements to underlying needs, and you build credibility through the depth of your understanding rather than the breadth of your pitch. • Uncovers business drivers. What outcome matters, what it’s worth, why now, and what happens if nothing changes. • Uncovers personal drivers. What each stakeholder is measured on, what they’re worried about, and what a win looks like for them individually. • Surfaces constraints early. Security, compliance, data access, budget, skills, politics, timelines — the things that kill projects in month two. • Aligns stakeholders. Spots conflicting goals between business, engineering, and risk, and brings them to a shared definition of success. • Asks strategic questions. Follows the answer with “why,” “how do you know,” and “what happens today when…” — without interrogating. Clients leave the conversation understanding their own problem better. • Earns authority through depth. Credibility comes from the quality of the questions and the insight in the playback, not from pitching technology. What You'll Do • Discover. Run workshops and process discovery with business and engineering stakeholders. Separate genuine AI problems from workflow problems wearing an AI costume. Come back with a scoped, defensible point of view. • Structure the process and the spec. For any business or engineering process being automated, map the current state, decision points, and exceptions, then turn it into specifications that both people and AI systems can execute against reliably. • Architect & design. Design agentic systems end to end — orchestration, context and retrieval architecture, tool and MCP integration, guardrails, human-in-the-loop checkpoints, and the boundaries between deterministic and non-deterministic components. • Build. Ship working systems — agents, multi-step automations, internal tooling, integration layers. You are hands-on. Prototypes that prove value in weeks, not slideware. • Engineer the delivery system. Own CI/CD for what you build: build, test, release, environments. • Consult. Present to and defend decisions in front of CTOs, VPs of Engineering, and business leadership. Quantify impact in their terms. Support pre-sales scoping and proposal work when the deal calls for it. What We're Looking For Engineering foundation • 8–10+ years of professional software development; back-end-heavy, full-stack capable. • Primary production experience in one of our core ecosystems: TypeScript / Node + React, Python, .NET / C#, or Java. • Strong in APIs, service design, event-driven / distributed systems, and data modeling. • Track record as a Solution Architect, Principal Engineer, or Tech Lead — can assess a current-state environment and define a credible roadmap. • Has designed and owned CI/CD — build, test, release, environments — not just used someone else’s pipeline. Applied AI • Has built and shipped LLM and agent systems beyond a demo. • Deep fluency with agentic tooling: Claude Code, agent frameworks, MCP, tool/function calling, multi-agent orchestration. • Context and spec engineering: structures codebases, specifications, process definitions, and context to produce reliable, context-efficient AI output. • Integration literacy: understands integration methods and options — APIs, webhooks, event streams, iPaaS (n8n, MuleSoft, etc.), MCP — and the constraints and strategies behind connecting AI to systems of record. Doesn’t need to own every integration, but can design around them and guide the team that does. Consulting & business • Demonstrates the discovery behaviors above: business and personal drivers, constraints, stakeholder alignment, strategic questioning. • Runs client conversations without an account manager in the room: workshops, proposal walkthroughs, hard questions, pushback. • Translates technical decisions into business outcomes — cycle time, cost per transaction, throughput, risk reduction, ROI. • Exceptional written and verbal communication; credible in front of senior stakeholders. Strong Plus • Production AI operations (Day 2): eval harnesses, observability and tracing, cost and token economics, drift, debugging non-deterministic systems. • Prompt- and eval-level regression testing running in CI and gating releases. • Delivery in regulated environments (financial services, healthcare, insurance, manufacturing). • Pre-sales support: discovery calls, SOW and estimate shaping, technical proposal defense. • Process mining, operations analysis, or transformation consulting background. • Client enablement and training — building the client team’s capability, not just the system. • Prior consulting or agency environment; comfort with ambiguity and multiple accounts. Work Authorization Requirements • Applicants must be currently authorized to work in the United States on a full-time basis. • FullStack will not sponsor applicants for work visas now or in the future. What We Offer • Competitive Salary. • Paid Time Off (vacation, sick leave, parental leave, holidays). • 100% remote work. • The ability to work with leading startups and Fortune 500 companies. • Health, dental, and vision insurance. • 401(k) w/ 4% match. • Ample opportunity for career advancement. • Continuing education opportunities. FullStack is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. Learn more about our Applicants Privacy Notice.

Design, build, and deploy AI-enabled systems embedded within client workflows, including discovery, architecture, and consulting. | 8-10+ years software development with strong TypeScript/Node/React skills, experience in system architecture, AI/LLM system building, and consulting with senior stakeholders. | About FullStack FullStack is your AI-native engineering partner, built to turn AI capability into certainty. Most companies can demo AI; few can get it to production and prove what it produced. We close that gap with three capabilities, AI built into your products from the start, elite vetted talent who apply it, and transparent execution that shows value every step of the way. With now over 600 customers in North America, FullStack is one integrated partner for all your AI engineering needs. With FullStack, you move forward with confidence. We’re Most Proud Of • Offering life-changing career opportunities to talented software professionals across the Americas. • Building highly-skilled software development teams for hundreds of the world’s greatest companies. • Having delivered hundreds of successful custom software solutions, which have positively impacted the lives and careers of millions of users. • Our 4.1-star rating on GlassDoor. • Our client Net Promoter Score of 68, twice the industry average. The Position Forward Deployed Engineers sit inside the client’s problem, not next to it. You embed with an enterprise team, find the work that actually warrants AI, architect the system, build it, and make it hold up in the real environment. This is a three-way role: consultant, operator, engineer. You should be as comfortable pressure-testing a business case with a COO or VP of Engineering as you are debugging why an agent’s tool-calling loop degraded after a context change. The people who succeed here can hold a discovery conversation on Monday and ship what they scoped on Thursday. On this track, your edge is application engineering: you make agents real inside the client’s actual systems, workflows, and delivery pipelines. We work with regulated industries and Fortune 500 clients who have AI mandates, real constraints, and low tolerance for demos that don’t hold up. How We Approach The Work FDEs are trusted to shape the solution, not only deliver it. Every engagement calls for architect-level judgment: • Reframes the request. Treats “we need an agent for X” as a hypothesis, not a spec. • Decomposes the problem. Outcome → process → decisions → data → systems → actors → constraints — before choosing technology. • Chooses the intervention. Remove, simplify, traditional software, automation, LLM, RAG, agent, or multi-agent — and defends why. • Owns the architecture decision. Documents trade-offs (ADRs), sets success metrics, pushes back on scope that won’t deliver value. Consultative Discovery Clients rarely arrive with a fully formed problem, and the FDE’s first contribution is helping them see it clearly. You lead discovery with genuine curiosity, moving the conversation from stated requirements to underlying needs, and you build credibility through the depth of your understanding rather than the breadth of your pitch. • Uncovers business drivers. What outcome matters, what it’s worth, why now, and what happens if nothing changes. • Uncovers personal drivers. What each stakeholder is measured on, what they’re worried about, and what a win looks like for them individually. • Surfaces constraints early. Security, compliance, data access, budget, skills, politics, timelines — the things that kill projects in month two. • Aligns stakeholders. Spots conflicting goals between business, engineering, and risk, and brings them to a shared definition of success. • Asks strategic questions. Follows the answer with “why,” “how do you know,” and “what happens today when…” — without interrogating. Clients leave the conversation understanding their own problem better. • Earns authority through depth. Credibility comes from the quality of the questions and the insight in the playback, not from pitching technology. What You'll Do • Discover. Run workshops and process discovery with business and engineering stakeholders. Separate genuine AI problems from workflow problems wearing an AI costume. Come back with a scoped, defensible point of view. • Structure the process and the spec. For any business or engineering process being automated, map the current state, decision points, and exceptions, then turn it into specifications that both people and AI systems can execute against reliably. • Architect & design. Design agentic systems end to end — orchestration, context and retrieval architecture, tool and MCP integration, guardrails, human-in-the-loop checkpoints, and the boundaries between deterministic and non-deterministic components. • Build. Ship working systems — agents, multi-step automations, internal tooling, integration layers. You are hands-on. Prototypes that prove value in weeks, not slideware. • Engineer the delivery system. Own CI/CD for what you build: build, test, release, environments. • Consult. Present to and defend decisions in front of CTOs, VPs of Engineering, and business leadership. Quantify impact in their terms. Support pre-sales scoping and proposal work when the deal calls for it. What We're Looking For Engineering foundation • 8–10+ years of professional software development; back-end-heavy, full-stack capable. • Primary production experience in one of our core ecosystems: TypeScript / Node + React, Python, .NET / C#, or Java. • Strong in APIs, service design, event-driven / distributed systems, and data modeling. • Track record as a Solution Architect, Principal Engineer, or Tech Lead — can assess a current-state environment and define a credible roadmap. • Has designed and owned CI/CD — build, test, release, environments — not just used someone else’s pipeline. Applied AI • Has built and shipped LLM and agent systems beyond a demo. • Deep fluency with agentic tooling: Claude Code, agent frameworks, MCP, tool/function calling, multi-agent orchestration. • Context and spec engineering: structures codebases, specifications, process definitions, and context to produce reliable, context-efficient AI output. • Integration literacy: understands integration methods and options — APIs, webhooks, event streams, iPaaS (n8n, MuleSoft, etc.), MCP — and the constraints and strategies behind connecting AI to systems of record. Doesn’t need to own every integration, but can design around them and guide the team that does. Consulting & business • Demonstrates the discovery behaviors above: business and personal drivers, constraints, stakeholder alignment, strategic questioning. • Runs client conversations without an account manager in the room: workshops, proposal walkthroughs, hard questions, pushback. • Translates technical decisions into business outcomes — cycle time, cost per transaction, throughput, risk reduction, ROI. • Exceptional written and verbal communication; credible in front of senior stakeholders. Strong Plus • Production AI operations (Day 2): eval harnesses, observability and tracing, cost and token economics, drift, debugging non-deterministic systems. • Prompt- and eval-level regression testing running in CI and gating releases. • Delivery in regulated environments (financial services, healthcare, insurance, manufacturing). • Pre-sales support: discovery calls, SOW and estimate shaping, technical proposal defense. • Process mining, operations analysis, or transformation consulting background. • Client enablement and training — building the client team’s capability, not just the system. • Prior consulting or agency environment; comfort with ambiguity and multiple accounts. Work Authorization Requirements • Applicants must be currently authorized to work in the United States on a full-time basis. • FullStack will not sponsor applicants for work visas now or in the future. What We Offer • Competitive Salary. • Paid Time Off (vacation, sick leave, parental leave, holidays). • 100% remote work. • The ability to work with leading startups and Fortune 500 companies. • Health, dental, and vision insurance. • 401(k) w/ 4% match. • Ample opportunity for career advancement. • Continuing education opportunities. FullStack is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. Learn more about our Applicants Privacy Notice.

Embed with client teams to architect, build, and operate AI-driven systems with a consultative approach. | Requires 8-10+ years in ML, data, and software engineering with production-grade Python skills and experience deploying and operating LLM and agent systems. | About FullStack FullStack is your AI-native engineering partner, built to turn AI capability into certainty. Most companies can demo AI; few can get it to production and prove what it produced. We close that gap with three capabilities, AI built into your products from the start, elite vetted talent who apply it, and transparent execution that shows value every step of the way. With now over 600 customers in North America, FullStack is one integrated partner for all your AI engineering needs. With FullStack, you move forward with confidence. We’re Most Proud Of • Offering life-changing career opportunities to talented software professionals across the Americas. • Building highly-skilled software development teams for hundreds of the world’s greatest companies. • Having delivered hundreds of successful custom software solutions, which have positively impacted the lives and careers of millions of users. • Our 4.1-star rating on GlassDoor. • Our client Net Promoter Score of 68, twice the industry average. The Position Forward Deployed Engineers sit inside the client’s problem, not next to it. You embed with an enterprise team, find the work that actually warrants AI, architect the system, build it, and stay long enough to make it survive contact with production. This is a three-way role: consultant, operator, engineer. You should be as comfortable pressure-testing a business case with a COO or VP of Engineering as you are diagnosing why retrieval quality collapsed after a data refresh. The people who succeed here can hold a discovery conversation on Monday and ship what they scoped on Thursday. On this track, your edge is data, retrieval, and model behavior: you make agents trustworthy by getting the data, context, and evaluation right. We work with regulated industries and Fortune 500 clients who have AI mandates, real constraints, and low tolerance for demos that don’t hold up. How We Approach The Work FDEs are trusted to shape the solution, not only deliver it. Every engagement calls for architect-level judgment: • Reframes the request. Treats “we need an agent for X” as a hypothesis, not a spec. • Decomposes the problem. Outcome → process → decisions → data → systems → actors → constraints — before choosing technology. • Chooses the intervention. Remove, simplify, traditional software, automation, LLM, RAG, agent, or multi-agent — and defends why. • Owns the architecture decision. Documents trade-offs (ADRs), sets success metrics, pushes back on scope that won’t deliver value. Consultative Discovery Clients rarely arrive with a fully formed problem, and the FDE’s first contribution is helping them see it clearly. You lead discovery with genuine curiosity, moving the conversation from stated requirements to underlying needs, and you build credibility through the depth of your understanding rather than the breadth of your pitch. • Uncovers business drivers. What outcome matters, what it’s worth, why now, and what happens if nothing changes. • Uncovers personal drivers. What each stakeholder is measured on, what they’re worried about, and what a win looks like for them individually. • Surfaces constraints early. Security, compliance, data access, budget, skills, politics, timelines — the things that kill projects in month two. • Aligns stakeholders. Spots conflicting goals between business, engineering, and risk, and brings them to a shared definition of success. • Asks strategic questions. Follows the answer with “why,” “how do you know,” and “what happens today when…” — without interrogating. Clients leave the conversation understanding their own problem better. • Earns authority through depth. Credibility comes from the quality of the questions and the insight in the playback, not from pitching technology. What You'll Do • Discover. Run workshops and process discovery with business, data, and engineering stakeholders. Assess data readiness honestly. Separate genuine AI problems from workflow problems wearing an AI costume. • Structure the process and the spec. For any business process being automated, map the current state, decision points, data inputs, and exceptions, then turn it into specifications that both people and AI systems can execute against reliably. • Architect & design. Design agentic systems end to end with emphasis on context and retrieval architecture — data sourcing, chunking, embeddings, hybrid search, reranking, permissions-aware retrieval, knowledge graphs where warranted — plus orchestration, MCP/tool integration, guardrails, and human-in-the-loop checkpoints. • Build. Ship working systems — agents, retrieval services, evaluation tooling, and the data flows that feed them. Prototypes that prove value in weeks, not slideware. • Engineer the delivery system. Own CI/CD for what you build, extended to AI: prompt and context versioning, eval suites that run in the pipeline and gate releases. • Operate (Day 2). Own what happens after launch — eval harnesses, groundedness and retrieval metrics, observability and tracing, cost and latency management, drift, and the handoff that lets the client’s team run it without you. • Consult. Present to and defend decisions in front of CTOs, VPs of Engineering, CDOs, and business leadership. Quantify impact in their terms. Support pre-sales scoping and proposal work when the deal calls for it. What We're Looking For Engineering foundation • 8–10+ years across ML, data, and software engineering. • Python-first, production-grade (typed, tested, deployed services — not notebooks only); working proficiency in TypeScript / Node, .NET / C#, or Java is a plus. • Track record as an ML/Data Architect, Principal Engineer, or Tech Lead — can assess a data and AI current state and define a credible roadmap. • Has designed and owned CI/CD for AI or ML systems (LLMOps / MLOps), not just application code handed off to others. Applied / operational AI • Has put LLM and agent systems into production and kept them running — not just proofs of concept or research models. • Deep fluency with retrieval and model behavior: embeddings, vector and hybrid search, reranking, structured output, model selection and routing; sound judgment on fine-tuning vs. prompting vs. retrieval. • Fluent with agentic tooling: Claude Code, agent frameworks, MCP, tool/function calling, multi-agent orchestration. • Context and spec engineering: designs what the model sees — retrieval, context construction, specifications, process definitions, and memory — for reliable, context-efficient output. • Day 2 experience: evaluation design (golden sets, LLM-as-judge, groundedness), observability, cost and token economics, failure-mode analysis. • Data and integration literacy: familiar with ETL/ELT patterns, warehouses/lakehouses (Snowflake, Databricks, BigQuery), and integration options (APIs, event streams, iPaaS, MCP) — enough to design around them, assess data readiness, and guide the data team. Does not need to be an ETL/ELT specialist. Consulting & business • Demonstrates the discovery behaviors above: business and personal drivers, constraints, stakeholder alignment, strategic questioning. • Runs client conversations without an account manager in the room: workshops, proposal walkthroughs, hard questions, pushback. • Translates technical decisions into business outcomes — accuracy, cycle time, cost per transaction, risk reduction, ROI. • Exceptional written and verbal communication; credible in front of senior stakeholders. Strong Plus • Delivery in regulated environments (financial services, healthcare, insurance, manufacturing); data governance and privacy experience. • Pre-sales support: discovery calls, SOW and estimate shaping, technical proposal defense. • Classical ML in production (forecasting, classification, anomaly detection) alongside LLM work. • Client enablement and training — building the client team’s capability, not just the system. • Prior consulting or agency environment; comfort with ambiguity and multiple accounts. Work Authorization Requirements • Applicants must be currently authorized to work in the country of this job posting on a full-time basis. • FullStack will not sponsor applicants for work visas now or in the future. What We Offer • Competitive Salary. • Paid Time Off (vacation, sick leave, parental leave, holidays). • 100% remote work. • The ability to work with leading startups and Fortune 500 companies. • Health, dental, and vision insurance. • 401(k) w/ 4% match. • Ample opportunity for career advancement. • Continuing education opportunities. FullStack is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. Learn more about our Applicants Privacy Notice.

Embed with client teams to architect, build, and operate AI-driven systems with a consultative approach. | Requires 8-10+ years in ML, data, and software engineering with production-grade Python and AI system deployment experience. | About FullStack FullStack is your AI-native engineering partner, built to turn AI capability into certainty. Most companies can demo AI; few can get it to production and prove what it produced. We close that gap with three capabilities, AI built into your products from the start, elite vetted talent who apply it, and transparent execution that shows value every step of the way. With now over 600 customers in North America, FullStack is one integrated partner for all your AI engineering needs. With FullStack, you move forward with confidence. We’re Most Proud Of • Offering life-changing career opportunities to talented software professionals across the Americas. • Building highly-skilled software development teams for hundreds of the world’s greatest companies. • Having delivered hundreds of successful custom software solutions, which have positively impacted the lives and careers of millions of users. • Our 4.1-star rating on GlassDoor. • Our client Net Promoter Score of 68, twice the industry average. The Position Forward Deployed Engineers sit inside the client’s problem, not next to it. You embed with an enterprise team, find the work that actually warrants AI, architect the system, build it, and stay long enough to make it survive contact with production. This is a three-way role: consultant, operator, engineer. You should be as comfortable pressure-testing a business case with a COO or VP of Engineering as you are diagnosing why retrieval quality collapsed after a data refresh. The people who succeed here can hold a discovery conversation on Monday and ship what they scoped on Thursday. On this track, your edge is data, retrieval, and model behavior: you make agents trustworthy by getting the data, context, and evaluation right. We work with regulated industries and Fortune 500 clients who have AI mandates, real constraints, and low tolerance for demos that don’t hold up. How We Approach The Work FDEs are trusted to shape the solution, not only deliver it. Every engagement calls for architect-level judgment: • Reframes the request. Treats “we need an agent for X” as a hypothesis, not a spec. • Decomposes the problem. Outcome → process → decisions → data → systems → actors → constraints — before choosing technology. • Chooses the intervention. Remove, simplify, traditional software, automation, LLM, RAG, agent, or multi-agent — and defends why. • Owns the architecture decision. Documents trade-offs (ADRs), sets success metrics, pushes back on scope that won’t deliver value. Consultative Discovery Clients rarely arrive with a fully formed problem, and the FDE’s first contribution is helping them see it clearly. You lead discovery with genuine curiosity, moving the conversation from stated requirements to underlying needs, and you build credibility through the depth of your understanding rather than the breadth of your pitch. • Uncovers business drivers. What outcome matters, what it’s worth, why now, and what happens if nothing changes. • Uncovers personal drivers. What each stakeholder is measured on, what they’re worried about, and what a win looks like for them individually. • Surfaces constraints early. Security, compliance, data access, budget, skills, politics, timelines — the things that kill projects in month two. • Aligns stakeholders. Spots conflicting goals between business, engineering, and risk, and brings them to a shared definition of success. • Asks strategic questions. Follows the answer with “why,” “how do you know,” and “what happens today when…” — without interrogating. Clients leave the conversation understanding their own problem better. • Earns authority through depth. Credibility comes from the quality of the questions and the insight in the playback, not from pitching technology. What You'll Do • Discover. Run workshops and process discovery with business, data, and engineering stakeholders. Assess data readiness honestly. Separate genuine AI problems from workflow problems wearing an AI costume. • Structure the process and the spec. For any business process being automated, map the current state, decision points, data inputs, and exceptions, then turn it into specifications that both people and AI systems can execute against reliably. • Architect & design. Design agentic systems end to end with emphasis on context and retrieval architecture — data sourcing, chunking, embeddings, hybrid search, reranking, permissions-aware retrieval, knowledge graphs where warranted — plus orchestration, MCP/tool integration, guardrails, and human-in-the-loop checkpoints. • Build. Ship working systems — agents, retrieval services, evaluation tooling, and the data flows that feed them. Prototypes that prove value in weeks, not slideware. • Engineer the delivery system. Own CI/CD for what you build, extended to AI: prompt and context versioning, eval suites that run in the pipeline and gate releases. • Operate (Day 2). Own what happens after launch — eval harnesses, groundedness and retrieval metrics, observability and tracing, cost and latency management, drift, and the handoff that lets the client’s team run it without you. • Consult. Present to and defend decisions in front of CTOs, VPs of Engineering, CDOs, and business leadership. Quantify impact in their terms. Support pre-sales scoping and proposal work when the deal calls for it. What We're Looking For Engineering foundation • 8–10+ years across ML, data, and software engineering. • Python-first, production-grade (typed, tested, deployed services — not notebooks only); working proficiency in TypeScript / Node, .NET / C#, or Java is a plus. • Track record as an ML/Data Architect, Principal Engineer, or Tech Lead — can assess a data and AI current state and define a credible roadmap. • Has designed and owned CI/CD for AI or ML systems (LLMOps / MLOps), not just application code handed off to others. Applied / operational AI • Has put LLM and agent systems into production and kept them running — not just proofs of concept or research models. • Deep fluency with retrieval and model behavior: embeddings, vector and hybrid search, reranking, structured output, model selection and routing; sound judgment on fine-tuning vs. prompting vs. retrieval. • Fluent with agentic tooling: Claude Code, agent frameworks, MCP, tool/function calling, multi-agent orchestration. • Context and spec engineering: designs what the model sees — retrieval, context construction, specifications, process definitions, and memory — for reliable, context-efficient output. • Day 2 experience: evaluation design (golden sets, LLM-as-judge, groundedness), observability, cost and token economics, failure-mode analysis. • Data and integration literacy: familiar with ETL/ELT patterns, warehouses/lakehouses (Snowflake, Databricks, BigQuery), and integration options (APIs, event streams, iPaaS, MCP) — enough to design around them, assess data readiness, and guide the data team. Does not need to be an ETL/ELT specialist. Consulting & business • Demonstrates the discovery behaviors above: business and personal drivers, constraints, stakeholder alignment, strategic questioning. • Runs client conversations without an account manager in the room: workshops, proposal walkthroughs, hard questions, pushback. • Translates technical decisions into business outcomes — accuracy, cycle time, cost per transaction, risk reduction, ROI. • Exceptional written and verbal communication; credible in front of senior stakeholders. Strong Plus • Delivery in regulated environments (financial services, healthcare, insurance, manufacturing); data governance and privacy experience. • Pre-sales support: discovery calls, SOW and estimate shaping, technical proposal defense. • Classical ML in production (forecasting, classification, anomaly detection) alongside LLM work. • Client enablement and training — building the client team’s capability, not just the system. • Prior consulting or agency environment; comfort with ambiguity and multiple accounts. Work Authorization Requirements • Applicants must be currently authorized to work in the country of this job posting on a full-time basis. • FullStack will not sponsor applicants for work visas now or in the future. What We Offer • Competitive Salary. • Paid Time Off (vacation, sick leave, parental leave, holidays). • 100% remote work. • The ability to work with leading startups and Fortune 500 companies. • Health, dental, and vision insurance. • 401(k) w/ 4% match. • Ample opportunity for career advancement. • Continuing education opportunities. FullStack is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. Learn more about our Applicants Privacy Notice.

Architect, build, and deploy AI agent systems embedded in client environments with consultative discovery and technical leadership. | Senior software developer with 8-10+ years experience, strong TypeScript/Node/React skills, solution architecture, AI/agent system development, and client consulting abilities. | About FullStack FullStack is your AI-native engineering partner, built to turn AI capability into certainty. Most companies can demo AI; few can get it to production and prove what it produced. We close that gap with three capabilities, AI built into your products from the start, elite vetted talent who apply it, and transparent execution that shows value every step of the way. With now over 600 customers in North America, FullStack is one integrated partner for all your AI engineering needs. With FullStack, you move forward with confidence. We’re Most Proud Of • Offering life-changing career opportunities to talented software professionals across the Americas. • Building highly-skilled software development teams for hundreds of the world’s greatest companies. • Having delivered hundreds of successful custom software solutions, which have positively impacted the lives and careers of millions of users. • Our 4.1-star rating on GlassDoor. • Our client Net Promoter Score of 68, twice the industry average. The Position Forward Deployed Engineers sit inside the client’s problem, not next to it. You embed with an enterprise team, find the work that actually warrants AI, architect the system, build it, and make it hold up in the real environment. This is a three-way role: consultant, operator, engineer. You should be as comfortable pressure-testing a business case with a COO or VP of Engineering as you are debugging why an agent’s tool-calling loop degraded after a context change. The people who succeed here can hold a discovery conversation on Monday and ship what they scoped on Thursday. On this track, your edge is application engineering: you make agents real inside the client’s actual systems, workflows, and delivery pipelines. We work with regulated industries and Fortune 500 clients who have AI mandates, real constraints, and low tolerance for demos that don’t hold up. How We Approach The Work FDEs are trusted to shape the solution, not only deliver it. Every engagement calls for architect-level judgment: • Reframes the request. Treats “we need an agent for X” as a hypothesis, not a spec. • Decomposes the problem. Outcome → process → decisions → data → systems → actors → constraints — before choosing technology. • Chooses the intervention. Remove, simplify, traditional software, automation, LLM, RAG, agent, or multi-agent — and defends why. • Owns the architecture decision. Documents trade-offs (ADRs), sets success metrics, pushes back on scope that won’t deliver value. Consultative Discovery Clients rarely arrive with a fully formed problem, and the FDE’s first contribution is helping them see it clearly. You lead discovery with genuine curiosity, moving the conversation from stated requirements to underlying needs, and you build credibility through the depth of your understanding rather than the breadth of your pitch. • Uncovers business drivers. What outcome matters, what it’s worth, why now, and what happens if nothing changes. • Uncovers personal drivers. What each stakeholder is measured on, what they’re worried about, and what a win looks like for them individually. • Surfaces constraints early. Security, compliance, data access, budget, skills, politics, timelines — the things that kill projects in month two. • Aligns stakeholders. Spots conflicting goals between business, engineering, and risk, and brings them to a shared definition of success. • Asks strategic questions. Follows the answer with “why,” “how do you know,” and “what happens today when…” — without interrogating. Clients leave the conversation understanding their own problem better. • Earns authority through depth. Credibility comes from the quality of the questions and the insight in the playback, not from pitching technology. What You'll Do • Discover. Run workshops and process discovery with business and engineering stakeholders. Separate genuine AI problems from workflow problems wearing an AI costume. Come back with a scoped, defensible point of view. • Structure the process and the spec. For any business or engineering process being automated, map the current state, decision points, and exceptions, then turn it into specifications that both people and AI systems can execute against reliably. • Architect & design. Design agentic systems end to end — orchestration, context and retrieval architecture, tool and MCP integration, guardrails, human-in-the-loop checkpoints, and the boundaries between deterministic and non-deterministic components. • Build. Ship working systems — agents, multi-step automations, internal tooling, integration layers. You are hands-on. Prototypes that prove value in weeks, not slideware. • Engineer the delivery system. Own CI/CD for what you build: build, test, release, environments. • Consult. Present to and defend decisions in front of CTOs, VPs of Engineering, and business leadership. Quantify impact in their terms. Support pre-sales scoping and proposal work when the deal calls for it. What We're Looking For Engineering foundation • 8–10+ years of professional software development; back-end-heavy, full-stack capable. • Primary production experience in one of our core ecosystems: TypeScript / Node + React, Python, .NET / C#, or Java. • Strong in APIs, service design, event-driven / distributed systems, and data modeling. • Track record as a Solution Architect, Principal Engineer, or Tech Lead — can assess a current-state environment and define a credible roadmap. • Has designed and owned CI/CD — build, test, release, environments — not just used someone else’s pipeline. Applied AI • Has built and shipped LLM and agent systems beyond a demo. • Deep fluency with agentic tooling: Claude Code, agent frameworks, MCP, tool/function calling, multi-agent orchestration. • Context and spec engineering: structures codebases, specifications, process definitions, and context to produce reliable, context-efficient AI output. • Integration literacy: understands integration methods and options — APIs, webhooks, event streams, iPaaS (n8n, MuleSoft, etc.), MCP — and the constraints and strategies behind connecting AI to systems of record. Doesn’t need to own every integration, but can design around them and guide the team that does. Consulting & business • Demonstrates the discovery behaviors above: business and personal drivers, constraints, stakeholder alignment, strategic questioning. • Runs client conversations without an account manager in the room: workshops, proposal walkthroughs, hard questions, pushback. • Translates technical decisions into business outcomes — cycle time, cost per transaction, throughput, risk reduction, ROI. • Exceptional written and verbal communication; credible in front of senior stakeholders. Strong Plus • Production AI operations (Day 2): eval harnesses, observability and tracing, cost and token economics, drift, debugging non-deterministic systems. • Prompt- and eval-level regression testing running in CI and gating releases. • Delivery in regulated environments (financial services, healthcare, insurance, manufacturing). • Pre-sales support: discovery calls, SOW and estimate shaping, technical proposal defense. • Process mining, operations analysis, or transformation consulting background. • Client enablement and training — building the client team’s capability, not just the system. • Prior consulting or agency environment; comfort with ambiguity and multiple accounts. Work Authorization Requirements • Applicants must be currently authorized to work in the United States on a full-time basis. • FullStack will not sponsor applicants for work visas now or in the future. What We Offer • Competitive Salary. • Paid Time Off (vacation, sick leave, parental leave, holidays). • 100% remote work. • The ability to work with leading startups and Fortune 500 companies. • Health, dental, and vision insurance. • 401(k) w/ 4% match. • Ample opportunity for career advancement. • Continuing education opportunities. FullStack is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. Learn more about our Applicants Privacy Notice.

Embed with client teams to architect, build, and deploy AI-enabled systems that solve real business problems. | 8-10+ years software development with strong backend and full-stack skills, experience in AI/LLM systems, solution architecture, CI/CD ownership, and client consulting. | About FullStack FullStack is your AI-native engineering partner, built to turn AI capability into certainty. Most companies can demo AI; few can get it to production and prove what it produced. We close that gap with three capabilities, AI built into your products from the start, elite vetted talent who apply it, and transparent execution that shows value every step of the way. With now over 600 customers in North America, FullStack is one integrated partner for all your AI engineering needs. With FullStack, you move forward with confidence. We’re Most Proud Of • Offering life-changing career opportunities to talented software professionals across the Americas. • Building highly-skilled software development teams for hundreds of the world’s greatest companies. • Having delivered hundreds of successful custom software solutions, which have positively impacted the lives and careers of millions of users. • Our 4.1-star rating on GlassDoor. • Our client Net Promoter Score of 68, twice the industry average. The Position Forward Deployed Engineers sit inside the client’s problem, not next to it. You embed with an enterprise team, find the work that actually warrants AI, architect the system, build it, and make it hold up in the real environment. This is a three-way role: consultant, operator, engineer. You should be as comfortable pressure-testing a business case with a COO or VP of Engineering as you are debugging why an agent’s tool-calling loop degraded after a context change. The people who succeed here can hold a discovery conversation on Monday and ship what they scoped on Thursday. On this track, your edge is application engineering: you make agents real inside the client’s actual systems, workflows, and delivery pipelines. We work with regulated industries and Fortune 500 clients who have AI mandates, real constraints, and low tolerance for demos that don’t hold up. How We Approach The Work FDEs are trusted to shape the solution, not only deliver it. Every engagement calls for architect-level judgment: • Reframes the request. Treats “we need an agent for X” as a hypothesis, not a spec. • Decomposes the problem. Outcome → process → decisions → data → systems → actors → constraints — before choosing technology. • Chooses the intervention. Remove, simplify, traditional software, automation, LLM, RAG, agent, or multi-agent — and defends why. • Owns the architecture decision. Documents trade-offs (ADRs), sets success metrics, pushes back on scope that won’t deliver value. Consultative Discovery Clients rarely arrive with a fully formed problem, and the FDE’s first contribution is helping them see it clearly. You lead discovery with genuine curiosity, moving the conversation from stated requirements to underlying needs, and you build credibility through the depth of your understanding rather than the breadth of your pitch. • Uncovers business drivers. What outcome matters, what it’s worth, why now, and what happens if nothing changes. • Uncovers personal drivers. What each stakeholder is measured on, what they’re worried about, and what a win looks like for them individually. • Surfaces constraints early. Security, compliance, data access, budget, skills, politics, timelines — the things that kill projects in month two. • Aligns stakeholders. Spots conflicting goals between business, engineering, and risk, and brings them to a shared definition of success. • Asks strategic questions. Follows the answer with “why,” “how do you know,” and “what happens today when…” — without interrogating. Clients leave the conversation understanding their own problem better. • Earns authority through depth. Credibility comes from the quality of the questions and the insight in the playback, not from pitching technology. What You'll Do • Discover. Run workshops and process discovery with business and engineering stakeholders. Separate genuine AI problems from workflow problems wearing an AI costume. Come back with a scoped, defensible point of view. • Structure the process and the spec. For any business or engineering process being automated, map the current state, decision points, and exceptions, then turn it into specifications that both people and AI systems can execute against reliably. • Architect & design. Design agentic systems end to end — orchestration, context and retrieval architecture, tool and MCP integration, guardrails, human-in-the-loop checkpoints, and the boundaries between deterministic and non-deterministic components. • Build. Ship working systems — agents, multi-step automations, internal tooling, integration layers. You are hands-on. Prototypes that prove value in weeks, not slideware. • Engineer the delivery system. Own CI/CD for what you build: build, test, release, environments. • Consult. Present to and defend decisions in front of CTOs, VPs of Engineering, and business leadership. Quantify impact in their terms. Support pre-sales scoping and proposal work when the deal calls for it. What We're Looking For Engineering foundation • 8–10+ years of professional software development; back-end-heavy, full-stack capable. • Primary production experience in one of our core ecosystems: TypeScript / Node + React, Python, .NET / C#, or Java. • Strong in APIs, service design, event-driven / distributed systems, and data modeling. • Track record as a Solution Architect, Principal Engineer, or Tech Lead — can assess a current-state environment and define a credible roadmap. • Has designed and owned CI/CD — build, test, release, environments — not just used someone else’s pipeline. Applied AI • Has built and shipped LLM and agent systems beyond a demo. • Deep fluency with agentic tooling: Claude Code, agent frameworks, MCP, tool/function calling, multi-agent orchestration. • Context and spec engineering: structures codebases, specifications, process definitions, and context to produce reliable, context-efficient AI output. • Integration literacy: understands integration methods and options — APIs, webhooks, event streams, iPaaS (n8n, MuleSoft, etc.), MCP — and the constraints and strategies behind connecting AI to systems of record. Doesn’t need to own every integration, but can design around them and guide the team that does. Consulting & business • Demonstrates the discovery behaviors above: business and personal drivers, constraints, stakeholder alignment, strategic questioning. • Runs client conversations without an account manager in the room: workshops, proposal walkthroughs, hard questions, pushback. • Translates technical decisions into business outcomes — cycle time, cost per transaction, throughput, risk reduction, ROI. • Exceptional written and verbal communication; credible in front of senior stakeholders. Strong Plus • Production AI operations (Day 2): eval harnesses, observability and tracing, cost and token economics, drift, debugging non-deterministic systems. • Prompt- and eval-level regression testing running in CI and gating releases. • Delivery in regulated environments (financial services, healthcare, insurance, manufacturing). • Pre-sales support: discovery calls, SOW and estimate shaping, technical proposal defense. • Process mining, operations analysis, or transformation consulting background. • Client enablement and training — building the client team’s capability, not just the system. • Prior consulting or agency environment; comfort with ambiguity and multiple accounts. Work Authorization Requirements • Applicants must be currently authorized to work in the United States on a full-time basis. • FullStack will not sponsor applicants for work visas now or in the future. What We Offer • Competitive Salary. • Paid Time Off (vacation, sick leave, parental leave, holidays). • 100% remote work. • The ability to work with leading startups and Fortune 500 companies. • Health, dental, and vision insurance. • 401(k) w/ 4% match. • Ample opportunity for career advancement. • Continuing education opportunities. FullStack is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. Learn more about our Applicants Privacy Notice.

Embed with client teams to architect, build, and operate AI-driven systems and consult on AI solutions. | 8-10+ years in ML/data/software engineering, Python production-grade coding, AI/ML system architecture, operational AI experience, and strong consulting skills. | About FullStack FullStack is your AI-native engineering partner, built to turn AI capability into certainty. Most companies can demo AI; few can get it to production and prove what it produced. We close that gap with three capabilities, AI built into your products from the start, elite vetted talent who apply it, and transparent execution that shows value every step of the way. With now over 600 customers in North America, FullStack is one integrated partner for all your AI engineering needs. With FullStack, you move forward with confidence. We’re Most Proud Of • Offering life-changing career opportunities to talented software professionals across the Americas. • Building highly-skilled software development teams for hundreds of the world’s greatest companies. • Having delivered hundreds of successful custom software solutions, which have positively impacted the lives and careers of millions of users. • Our 4.1-star rating on GlassDoor. • Our client Net Promoter Score of 68, twice the industry average. The Position Forward Deployed Engineers sit inside the client’s problem, not next to it. You embed with an enterprise team, find the work that actually warrants AI, architect the system, build it, and stay long enough to make it survive contact with production. This is a three-way role: consultant, operator, engineer. You should be as comfortable pressure-testing a business case with a COO or VP of Engineering as you are diagnosing why retrieval quality collapsed after a data refresh. The people who succeed here can hold a discovery conversation on Monday and ship what they scoped on Thursday. On this track, your edge is data, retrieval, and model behavior: you make agents trustworthy by getting the data, context, and evaluation right. We work with regulated industries and Fortune 500 clients who have AI mandates, real constraints, and low tolerance for demos that don’t hold up. How We Approach The Work FDEs are trusted to shape the solution, not only deliver it. Every engagement calls for architect-level judgment: • Reframes the request. Treats “we need an agent for X” as a hypothesis, not a spec. • Decomposes the problem. Outcome → process → decisions → data → systems → actors → constraints — before choosing technology. • Chooses the intervention. Remove, simplify, traditional software, automation, LLM, RAG, agent, or multi-agent — and defends why. • Owns the architecture decision. Documents trade-offs (ADRs), sets success metrics, pushes back on scope that won’t deliver value. Consultative Discovery Clients rarely arrive with a fully formed problem, and the FDE’s first contribution is helping them see it clearly. You lead discovery with genuine curiosity, moving the conversation from stated requirements to underlying needs, and you build credibility through the depth of your understanding rather than the breadth of your pitch. • Uncovers business drivers. What outcome matters, what it’s worth, why now, and what happens if nothing changes. • Uncovers personal drivers. What each stakeholder is measured on, what they’re worried about, and what a win looks like for them individually. • Surfaces constraints early. Security, compliance, data access, budget, skills, politics, timelines — the things that kill projects in month two. • Aligns stakeholders. Spots conflicting goals between business, engineering, and risk, and brings them to a shared definition of success. • Asks strategic questions. Follows the answer with “why,” “how do you know,” and “what happens today when…” — without interrogating. Clients leave the conversation understanding their own problem better. • Earns authority through depth. Credibility comes from the quality of the questions and the insight in the playback, not from pitching technology. What You'll Do • Discover. Run workshops and process discovery with business, data, and engineering stakeholders. Assess data readiness honestly. Separate genuine AI problems from workflow problems wearing an AI costume. • Structure the process and the spec. For any business process being automated, map the current state, decision points, data inputs, and exceptions, then turn it into specifications that both people and AI systems can execute against reliably. • Architect & design. Design agentic systems end to end with emphasis on context and retrieval architecture — data sourcing, chunking, embeddings, hybrid search, reranking, permissions-aware retrieval, knowledge graphs where warranted — plus orchestration, MCP/tool integration, guardrails, and human-in-the-loop checkpoints. • Build. Ship working systems — agents, retrieval services, evaluation tooling, and the data flows that feed them. Prototypes that prove value in weeks, not slideware. • Engineer the delivery system. Own CI/CD for what you build, extended to AI: prompt and context versioning, eval suites that run in the pipeline and gate releases. • Operate (Day 2). Own what happens after launch — eval harnesses, groundedness and retrieval metrics, observability and tracing, cost and latency management, drift, and the handoff that lets the client’s team run it without you. • Consult. Present to and defend decisions in front of CTOs, VPs of Engineering, CDOs, and business leadership. Quantify impact in their terms. Support pre-sales scoping and proposal work when the deal calls for it. What We're Looking For Engineering foundation • 8–10+ years across ML, data, and software engineering. • Python-first, production-grade (typed, tested, deployed services — not notebooks only); working proficiency in TypeScript / Node, .NET / C#, or Java is a plus. • Track record as an ML/Data Architect, Principal Engineer, or Tech Lead — can assess a data and AI current state and define a credible roadmap. • Has designed and owned CI/CD for AI or ML systems (LLMOps / MLOps), not just application code handed off to others. Applied / operational AI • Has put LLM and agent systems into production and kept them running — not just proofs of concept or research models. • Deep fluency with retrieval and model behavior: embeddings, vector and hybrid search, reranking, structured output, model selection and routing; sound judgment on fine-tuning vs. prompting vs. retrieval. • Fluent with agentic tooling: Claude Code, agent frameworks, MCP, tool/function calling, multi-agent orchestration. • Context and spec engineering: designs what the model sees — retrieval, context construction, specifications, process definitions, and memory — for reliable, context-efficient output. • Day 2 experience: evaluation design (golden sets, LLM-as-judge, groundedness), observability, cost and token economics, failure-mode analysis. • Data and integration literacy: familiar with ETL/ELT patterns, warehouses/lakehouses (Snowflake, Databricks, BigQuery), and integration options (APIs, event streams, iPaaS, MCP) — enough to design around them, assess data readiness, and guide the data team. Does not need to be an ETL/ELT specialist. Consulting & business • Demonstrates the discovery behaviors above: business and personal drivers, constraints, stakeholder alignment, strategic questioning. • Runs client conversations without an account manager in the room: workshops, proposal walkthroughs, hard questions, pushback. • Translates technical decisions into business outcomes — accuracy, cycle time, cost per transaction, risk reduction, ROI. • Exceptional written and verbal communication; credible in front of senior stakeholders. Strong Plus • Delivery in regulated environments (financial services, healthcare, insurance, manufacturing); data governance and privacy experience. • Pre-sales support: discovery calls, SOW and estimate shaping, technical proposal defense. • Classical ML in production (forecasting, classification, anomaly detection) alongside LLM work. • Client enablement and training — building the client team’s capability, not just the system. • Prior consulting or agency environment; comfort with ambiguity and multiple accounts. Work Authorization Requirements • Applicants must be currently authorized to work in the country of this job posting on a full-time basis. • FullStack will not sponsor applicants for work visas now or in the future. What We Offer • Competitive Salary. • Paid Time Off (vacation, sick leave, parental leave, holidays). • 100% remote work. • The ability to work with leading startups and Fortune 500 companies. • Health, dental, and vision insurance. • 401(k) w/ 4% match. • Ample opportunity for career advancement. • Continuing education opportunities. FullStack is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. Learn more about our Applicants Privacy Notice.