What are the responsibilities and job description for the Agentic AI Developer (Python) Vertex AI RAG + Graph/Vector Datastores position at Envision Technology Solutions?
Dear Application,
Please let me know if you are interested.
Title: Agentic AI Developer (Python) - Vertex AI RAG Graph/Vector Datastores
Location: Berkeley Heights, NJ (5 Days Onsite)
Hire Type: Long Term Contract
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).