AI Product Engineer

Lynon

- Turn business workflows into production pipelines: generation → QA → publish → feedback loops - Compose multi-step agent behavior with model-native tool calling, structured outputs, and MCP-based integrations — reaching for a framework only when it clearly earns its place - Orchestrate across providers (Claude, GPT, Gemini, others) with retries, fallbacks, structured outputs, and per-run cost tracking - Develop RAG and memory layers (pgvector / Qdrant / OpenSearch + Postgres) - Integrate agents with the tools teams already use: Jira, CRM, Notion/Confluence, repositories, analytics

- 4–6+ years across software engineering, backend, DevOps, or automation — comfortable owning something end to end, from API to deployment - Hands-on experience building LLM-powered applications: RAG, tool calling, and multi-step agent workflows on top of frontier models - A real, demonstrable focus on evaluation and safety — you’ve measured output quality, not just eyeballed it - Strong reliability fundamentals: retries, queues/workers, rate limits, cost/latency trade-offs - Working knowledge of LLM APIs, vector search, structured outputs, and MCP / tool-calling integrations - Python (FastAPI a plus), Docker, CI/CD, and observability tooling - Pragmatic and product-minded: you reach for the simplest thing that works, and you ship

Apply here https://lynon.bamboohr.com/careers/109?source=aWQ9MjQ%3D Please mention in your application that you have learned about this position from MyJob.am