mProgen

Compensation

Full Description

We are seeking an experienced and forward-thinking AI Lead Engineer to join our AI Ops delivery team. In this role, you will lead the design, development, and delivery of GenAI-powered agentic systems that automate complex business processes across domains such as HR, Payroll, SAP, and Client Delivery. You ll serve as the technical lead within a Business/Core Pod, responsible for mentoring engineers, shaping solution architectures, and ensuring delivery excellence. This role combines deep hands-on engineering with architectural ownership and close collaboration with product managers and domain experts. Key Responsibilities Own the technical design and architecture of GenAI agent workflows within the Business Pod Serve as a hands-on developer, actively contributing to the design, coding, and delivery of production-grade agents and tools Lead code and design reviews, ensuring alignment with architectural standards and platform best practices Collaborate with the AI Ops Core Pod to adopt and influence reusable assets, frameworks, and integration patterns Design and implement LLM-powered agent workflows using frameworks such as LangChain, LangGraph, or CrewAI Develop prompt chains, agent tools, and custom modules that support reasoning, summarization, and multi-step task execution Integrate agents with enterprise systems (e.g., Workday, SAP, Salesforce) via REST APIs, SDKs, or message queues Build and manage agent lifecycle components, including initialization, memory/state handling, and fallback logic Implement and consume vector store integrations, prompt templates, and retrieval-augmented generation (RAG) techniques Ensure workflows are robust, secure, and observable, with proper logging, monitoring, and exception handling Partner with Product Managers, SMEs, and QA to translate business processes into agentic workflows and iterate based on feedback Contribute to the automation of testing, deployment, and validation pipelines for AI agents Maintain thorough documentation of agent behavior, design decisions, and integration logic for operational readiness and knowledge transfer Preferred Qualifications Bachelor s or Master s degree in Computer Science, Software Engineering, or a related technical field 4+ years of experience in developing and deploying production-grade AI/ML or automation solutions Strong proficiency in Python, with hands-on experience using FastAPI, REST APIs, and background task orchestration Deep familiarity with agentic frameworks such as LangChain, LangGraph, CrewAI, ReAct, or similar Understanding of LLM orchestration, including prompt design, tool usage, context management, and agent memory Experience integrating with enterprise systems via APIs, event/message queues (e.g., Kafka, Service Bus), and webhooks Solid foundation in distributed system design, including state management, retries, error handling, and resilience Experience with business process automation or workflow automation in real-world environments Comfortable working with SQL/NoSQL databases, including data modeling and validation Knowledge of containerization (Docker), orchestration platforms (Kubernetes), and deployment to cloud platforms (Azure, AWS, or Google Cloud Platform) Understanding of security concepts including authentication (OAuth2), authorization, and secure API integration Exposure to multi-agent patterns (e.g., supervisor-worker, planner-executor, judge-critic) is a strong plusNice to Have Contributions to open-source agent frameworks or AI tooling. Experience working with observability and monitoring tools to track agent performance. Exposure to knowledge graphs, memory management systems, or retrieval-augmented generation (RAG) pipelines.