Senior AI Engineer, Agentic and RAG Systems

Responsibilities

• Design agent orchestration (graph/state, conditional routing, tool calling, memory, checkpointing) in LangGraph / LangChain or equivalent
• Build production RAG end-to-end: chunking, embeddings, vector stores, hybrid retrieval, reranking, caching, and grounded synthesis
• Own Python / FastAPI services – async, SSE streaming, session handling, and structured error contracts Instrument with tracing and evaluation harnesses (MLflow, OpenTelemetry, or equivalent) for accuracy, cost, and regression Ship on Docker + Kubernetes (EKS/AKS/GKE) via CI/CD with test, eval, and canary gates
• Drive LLM cost engineering – model routing, prompt optimization, caching, token accounting, and build-vs-buy decisions Apply GenAI safety & governance: hallucination control, prompt-injection defense, PII handling, and HITL where required
• Partner with data engineering on semantic layers and pipelines (PySpark / SQL where applicable)

Languages