AI/ML with Agentic AI Developer (Python)

Note: Need Only Local to NY/NJ/TX, Who can attend F2F Interview.

Position: AI/ML with Agentic AI Developer (Python) Location: NY/NJ/TX - (Onsite - Need Only Locals) Duration: Lon 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).

Thanks & Regards
Mahesh