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,



protected 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. (ref:hirist.tech)

📌 Agentic AI Engineer (Gurugram)
🏢 Highbury Consulting Services
📍 Gurugram