Agentic AI Lead

Role : Agentic AI Lead (Python) — Vertex AI RAG + Graph/Vector Datastores
Location : Berkeley Heights, NJ (All 5 Days a week Onsite)
Duration : Full Time

Find out if this opportunity is a good fit by reading all of the information that follows below.

Role summa
ryWe’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 → deploymen
t.What you’ll
doDesign 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 framework
s.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 contro
l.Define and run evaluation (retrieval metrics, answer quality, hallucination/grounding checks), and improve system quality iterativel
y.Ship to production: APIs, monitoring/observability, cost/performance optimization, CI/CD, and security best practice
s.Must-have skil
lsStrong 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 pattern
s.Solid knowledge of vector search concepts and at least one vector DB in productio
n.Comfortable with graph data modeling and graph querying (Cypher/Gremlin/SPARQL basics
).Strong engineering practices: code reviews, testing, telemetry, secure-by-design, reliability mindse
t.Nice-to-ha
veKnowledge graphs for RAG (entity linking, graph traversal + retrieval fusion
).Streaming/messaging (Pub/Sub, Kafka), document pipelines (Document AI), and multilingual retrieva
l.Experience with evaluation tooling (RAGAS, TruLens, custom eval harnesses), prompt/version managemen
t.Frontend integration (basic React/Next.js) or platform enablement (internal developer tooling

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We are an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, citizenship status, age, disability, genetic information, protected veteran status, or any other characteristic protected by applicable law.