Agentic AI Developer (Python) — Vertex AI RAG + Graph/Vector Datastores

Dice is the leading career destination for tech experts at every stage of their careers. Our client, UniqueHire Consulting LLC, is seeking the following. Apply via Dice today! Job Title: Agentic AI Developer (Python) — Vertex AI RAG + Graph/Vector Datastores Location: Berkeley Heights, NJ - Onsite Duration: 12+ Months Contract Note: PP Number is Must No OPT/CPT/H1B T/ EAD Must be Local Job Description: 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).