Agentic AI Lead (Python)

Design and implement RAG pipelines on Google Cloud / Vertex AI (chunking, embeddings, indexing, retrieval, reranking, grounding). Build agentic workflows (tool use, planning, reflection/guardrails, structured outputs) using Python-first frameworks. Integrate agents with Graph DBs (e.g., Neo4j, JanusGraph, Neptune) and Vector DBs (e.g., Vertex Vector Search, Pinecone, Weaviate, Milvus, pgvector). Create robust data ingestion/ETL from PDFs, docs, webpages, and internal sources; implement metadata strategy and access control. Define and run evaluation (retrieval metrics, answer quality, hallucination/grounding checks), and improve system quality iteratively. Ship to production: APIs, monitoring/observability, cost/performance optimization, CI/CD, and security best practices. Must-have skills Strong Python (clean architecture, async, testing, typing, packaging). Proven experience building RAG solutions (hybrid search, reranking, chunking strategies, embeddings, prompt + schema design). Hands-on with Vertex AI and GCP fundamentals (IAM, logging/monitoring, Cloud Run/GKE, storage). Experience with at least one agentic framework (e.g., LangGraph/LangChain, LlamaIndex, Semantic Kernel, AutoGen) and tool/function calling patterns. Solid knowledge of vector search concepts and at least one vector DB in production. Comfortable with graph data modeling and graph querying (Cypher/Gremlin/SPARQL basics). Strong engineering practices: code reviews, testing, telemetry, secure-by-design, reliability mindset. J-18808-Ljbffr