Agentic AI Developer; Python Vertex AI RAG Graph​/Vector Datastores

Title:

Agentic AI Developer (Python) - Vertex AI RAG + Graph/Vector Data stores

Location:

Berkeley Heights, NJ (5 Days Onsite)

Hire Type:
Long Term Contract

We're looking for a strong agentic AI developer who can build and product ionize Vertex AI based RAG systems, design reliable tool‑using agents, and work comfortably with vector databases and graph databases. You will own end‑to‑end delivery: ingestion, retrieval, agent orchestration, evaluation, and deployment.

• 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, Janus Graph, 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.

• 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., Lang Graph/Lang Chain, Llama Index, Semantic Kernel, Auto Gen) 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.

• 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, Tru Lens, custom eval harnesses), prompt/version management.
• Frontend integration (basic React/Next.js) or platform enablement (internal developer tooling).