Agentic AI Developer

Agentic AI Developer (Python) Vertex AI RAG + Graph/Vector Datastores Berkeley Heights, NJ 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 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 Google Cloud Platform 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. Nice-to-have Knowledge graphs for RAG (entity linking, graph traversal + retrieval fusion). Streaming/messaging (Pub/Sub, Kafka), document pipelines (Document AI), and multilingual retrieval. Experience with evaluation tooling (RAGAS, TruLens, custom eval harnesses), prompt/version management. Frontend integration (basic React/Next.js) or platform enablement (internal developer tooling).