Agentic AI Lead

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

Duration: Full Time

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

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 framework

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 practice

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 pattern

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 mindsets

Nice-to-haves

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)

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. https://www.e-verify.gov/sites/default/files/everify/posters/IER_RighttoWorkPoster.

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