GenAI architect

Agentic AI Lead/ Architect (Python) — Vertex AI RAG + Graph/Vector Datastores Role summary We’re looking for a strong agentic AI developer who can build and productionize Vertex AI–based RAG systems (Vertex AI Search / Vertex AI RAG patterns), design reliable tool-using agents, and work comfortably with vector databases and graph databases. You’ll own end-to-end delivery: ingestion → retrieval → agent orchestration → evaluation → deployment. What you’ll do 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 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