Telnyx

Design and build multi-agent AI systems in Python to automate complex workflows and manage backend service lifecycles. | Requires 2+ years building production-grade AI agent systems with strong Python and container deployment skills. | About Telnyx Telnyx is an industry leader that's not just imagining the future of global connectivity—we're building it. From architecting and amplifying the reach of a private, global, multi-cloud IP network, to bringing hyperlocal edge technology right to your fingertips through intuitive APIs, we're shaping a new era of seamless interconnection between people, devices, and applications. We're driven by a desire to transform and modernize what's antiquated, automate the manual, and solve real-world problems through innovative connectivity solutions. As a testament to our success, we're proud to stand as a financially stable and profitable company. Our robust profitability allows us not only to invest in pioneering technologies but also to foster an environment of continuous learning and growth for our team. Our collective vision is a world where borderless connectivity fuels limitless innovation. By joining us, you can be part of laying the foundations for this interconnected future. We're currently seeking passionate individuals who are excited about the opportunity to contribute to an industry-shaping company while growing their own skills and careers. About the Team The RevOps team owns the systems layer, operations & automation that supports Telnyx's growth engine. Historically, that meant administering GTM tools used by humans: Salesforce, marketing automation, enrichment vendors, routing, campaign workflows, reporting, and vendor integrations. That operating model is changing. Telnyx is increasingly building AI agents and automation that interact directly with the GTM stack. The systems team now needs to support both human-facing workflows and bot-facing infrastructure: clean data, reliable integrations, durable automations, documented process, and scalable operating patterns. About the Role We're looking for a Software Engineer who builds and operates the AI-native backend systems powering our go-to-market motion. You'll design multi-agent architectures, build reliable integrations across complex business systems, and own services end-to-end from prototype through production. The systems you build orchestrate LLM-powered agents that handle real business workflows — qualifying leads, generating emails, routing meetings, enriching contacts, and managing outbound campaigns. These are stateful, multi-step agent systems running on Kubernetes that make decisions, call tools, and interact with external APIs under real constraints: rate limits, token budgets, cost targets, and data quality issues. You'll partner with Engineering Leads and Technical Product Managers to understand the problem space, then translate those problems into well-architected, observable, and maintainable software. This isn't prompt engineering and it isn't gluing together SaaS tools - it's systems engineering with AI as a core primitive. This is a hands-on builder role with high ownership. You'll make architectural decisions, ship iteratively, debug production issues, and care deeply about what happens after code merges. Responsibilities Design and build multi-agent AI systems in Python that handle complex, multi-step business workflows - qualification, email generation, routing, enrichment, and outbound orchestration Architect model-agnostic abstraction layers that decouple business logic from LLM providers, enabling flexibility across Claude, GPT, and open-source models Build and operate backend services (FastAPI/Flask) deployed on Kubernetes with CI/CD, managing the full lifecycle from deployment configuration to production reliability Design tool-use patterns for AI agents - structured function calling, multi-step reasoning, state management across conversation turns, and graceful handling of model failures Build integrations across external systems (CRM, enrichment APIs, outreach platforms, Slack) with proper error handling, retries, rate limiting, and data contracts Instrument and monitor AI systems in production — build observability into agent behavior, track success rates, detect regressions, and debug non-deterministic failures Design and run experiments (A/B tests, prompt variations, model comparisons) with proper evaluation infrastructure to measure what's actually working Requirements 2+ years of software engineering experience building backend services in Python Production experience building multi-step AI agent systems — stateful workflows where models make decisions, call tools, and operate across multiple turns, not single-shot API wrappers Strong understanding of LLM internals as they affect system design: context window management, token budgets, cost/latency/capability tradeoffs across models, structured outputs, and strategies for handling hallucination and refusals Experience testing and evaluating non-deterministic AI systems — you understand that assert output == expected doesn't work and have built or used alternatives Solid software architecture fundamentals: API design, state management, fault tolerance, and graceful degradation when upstream services fail Production experience with containerized deployments (Docker, Kubernetes) and CI/CD pipelines Experience integrating with external APIs at scale — auth flows, rate limiting, retries, data normalization, and managing the operational complexity of multiple third-party dependencies Proficiency with SQL and data systems for building targeting, enrichment, and analytics pipelines Built observability into production systems — structured logging, tracing, alerting, and monitoring that you actually use to debug issues High ownership: you deploy your own code, investigate your own incidents, and close the loop between what you shipped and how it performs Nice to Have Experience with specific GTM/RevOps systems (Salesforce, Apollo, Lusha, enrichment providers) or similar complex business platforms Background in growth engineering, marketing automation, or revenue operations tooling Experience with Slack bot development or conversational AI interfaces Contributions to or experience with open-source AI agent frameworks Familiarity with ArgoCD, StatefulSets, or Kubernetes operations beyond basic deployments

Develop and maintain microservices for high-volume data pipelines and architect low latency systems with fraud detection and multi-cloud deployments. | Bachelor's degree with 4+ years in Java ecosystem, proficiency in Cassandra/ClickHouse/OpenSearch, and experience with distributed systems and functional programming. | About Telnyx Telnyx is an industry leader that's not just imagining the future of global connectivity—we're building it. From architecting and amplifying the reach of a private, global, multi-cloud IP network, to bringing hyperlocal edge technology right to your fingertips through intuitive APIs, we're shaping a new era of seamless interconnection between people, devices, and applications. We're driven by a desire to transform and modernize what's antiquated, automate the manual, and solve real-world problems through innovative connectivity solutions. As a testament to our success, we're proud to stand as a financially stable and profitable company. Our robust profitability allows us not only to invest in pioneering technologies but also to foster an environment of continuous learning and growth for our team. Our collective vision is a world where borderless connectivity fuels limitless innovation. By joining us, you can be part of laying the foundations for this interconnected future. We're currently seeking passionate individuals who are excited about the opportunity to contribute to an industry-shaping company while growing their own skills and careers. Our backend team is building a highly-available and distributed data pipeline that powers Telnyx products like Voice, Messaging and Wireless. We leverage the Telnyx private backbone network to build a cloud-agnostic data platform that processes thousands of messages per second. With our lean team, we're currently running and managing more than 30 dockerized microservices deployed in multiple regions in the U.S. and Europe. We are looking for a senior Java backend engineer with deep knowledge of Java Core. In this role you will Work on 30+ microservices implemented in Java 11 and Scala that process, aggregate and expose all data our telecommunication and messaging engines generate Improve performance of our pipeline — we currently process 10k messages/sec and are growing Architect our services and pipelines so they work with minimum latency in all our locations around the world (U.S., E.U., Australia, South America) Implement fraud detection processes with machine learning (Kafka and Kafka Streams) Deploy services to multi-cloud Kubernetes environments to achieve 99.999% uptime Push code to production multiple times a day What we're looking for Degree in Computer Science, Information Technology, or equivalent work experience 4+ years of experience with Java and its ecosystem of tooling Experience with at least one of these databases: Cassandra, ClickHouse, or OpenSearch Experience with AI tooling, or a strong willingness to learn Nice to have Experience with Scala or another functional programming language Experience with Linux, Git, and Docker in a CI environment Experience with distributed systems and systems that maintain "five nines" (99.999%) of uptime LI-KG1 LI-DUBLIN LI-BRAZIL