Lead AI Engineer, Agentic and RAG Systems

Responsibilities

• Own the end-to-end architecture of GenAI platforms across multiple services and teams, defining standards, patterns, and reference implementations
• Lead the design of agent orchestration (graph/state, conditional routing, tool calling, memory, checkpointing) in LangGraph / LangChain or equivalent, and set best practices for the team Architect production RAG end-to-end: chunking, embeddings, vector stores, hybrid retrieval, reranking, caching, and grounded…
• Drive the design and delivery of Python / FastAPI services – async, SSE streaming, session handling, and structured error contracts – establishing service templates and conventions Define the observability and evaluation strategy (MLflow, OpenTelemetry, or equivalent) for accuracy, cost, and regression across the…
• Own the deployment platform on Docker + Kubernetes (EKS/AKS/GKE) with CI/CD, test, eval, and canary gates – setting release standards for AI systems
• Lead LLM cost engineering strategy – model routing, prompt optimization, caching, token accounting, and build-vs-buy decisions at portfolio level Establish GenAI safety & governance practices: hallucination control, prompt-injection defense, PII handling, and HITL where required
• Partner with data engineering leadership on semantic layers and pipelines (PySpark / SQL where applicable), and align roadmaps across teams Mentor and grow senior and mid-level engineers through design reviews, pairing, and technical coaching; conduct hiring and technical interviews Represent engineering in…

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