Agentic AI Developer (Python)

New York, NY, US • Posted 5 hours ago • Updated 5 hours ago

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Title: Agentic AI Developer (Python) — Vertex AI RAG + Graph/Vector Datastores
Location: Berkeley Heights, NJ (5 days onsite)
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).

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 10117909
Position Id: 26-02224
Posted 5 hours ago

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Agentic AI Developer (Python)

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