What you will do :

- Build multi-agent workflows using LangGraph or similar - routing, state, tool use, human-in-the-loop review.

- Build retrieval systems over domain data: chunking, hybrid search, reranking, and knowledge-graph or ontology-based retrieval where it helps.

- Evaluate before you ship - test harnesses, LLM-as-judge, regression suites - and monitor quality once it is live.

- Handle regulated data properly: de-identification, PHI controls, audit trails, guardrails on inputs and outputs.

- Deploy and run what you build - containers, CI/CD, cloud, safe rollback.

- Work directly with clinical and domain experts to turn unclear workflows into something a system can actually do.

Must have :

- Strong Python and production API experience (FastAPI or similar).

- At least one agentic system you built that real users depend on - you can explain the design, what broke, and what you would change.

- Real RAG engineering, not just a vector store call. Retrieval quality work with numbers behind it.

- Working knowledge of an LLM evaluation and observability stack (LangSmith, Langfuse, Arize, RAGAS or equivalent).

- Docker plus one cloud platform (AWS, Azure or GCP), and comfort owning a service end to end.

- Judgment about when an agent is the wrong answer and a simple pipeline is the right one.