Envision Technology Solutions
Agentic AI Developer (Python) — Vertex AI RAG + Graph/Vector Datastores
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an "Agentic AI Developer (Python)" in Berkeley Heights, NJ, with a contract length of "unknown" and a pay rate of "unknown." Key skills include strong Python, RAG solutions, Vertex AI, and experience with vector and graph databases.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
April 1, 2026
🕒 - Duration
Unknown
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🏝️ - Location
On-site
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
Berkeley Heights, NJ
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🧠 - Skills detailed
#Indexing #Code Reviews #Kafka (Apache Kafka) #Observability #AI (Artificial Intelligence) #Databases #Deployment #Knowledge Graph #React #Strategy #IAM (Identity and Access Management) #Metadata #Cloud #Storage #GCP (Google Cloud Platform) #Python #Data Ingestion #Data Strategy #Data Modeling #"ETL (Extract #Transform #Load)" #Schema Design #Security #Neo4J #Graph Databases #Langchain #Monitoring #Logging
Role description
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 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).
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 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).
