> Markdown version of [/jobs/ext/4567091-agentic-ai-developer](https://www.wearedevelopers.com/jobs/ext/4567091-agentic-ai-developer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Agentic AI Developer

- **Company:** HireKeyz Inc
- **Location:** Berkeley Heights, NJ, United States
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Multitier Architecture, Application Programming Interfaces (APIs), Artificial Intelligence, Code Review, Continuous Integration, Extract Transform Load (ETL), Programming Tools, Graph Database, Identity and Access Management, Information Retrieval, Python (Programming Language), Neo4j, Performance Tuning, Entity Linking, Next.js, Search Technologies, SPARQL, Data Streaming, Data Logging, Pinecone, Google Cloud, Data Ingestion, LangChain, ReactJS, Retrieval-Augmented Generation, Llamaindex, Indexer, Agentic-AI, Vertex AI Vector Search, Pgvector, RAGAS (Retrieval Augmented Generation Assessment), Weaviate, AutoGen, Apache Kafka, Milvus, Front End Software Development, Invoking Functions, Semantic Kernel, Intelligent Document Processing, Software Version Control
- **Published:** November 4, 2026
- **Apply:** https://www.thejobnetwork.com/job/42bb5b12-3ba0-4164-adb9-2bf73c30428a/agentic-ai-developer

## About the Role

* 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 GCP 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).

## Description

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.