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Vector Databases Jobs in New York (NOW HIRING)

JConnect Infotech

AI/ML Engineer

Passaic, NJ · On-site

... Vector database integration • Databases o Vector Databases o MongoDB • Production-grade ML Engineering o Scalable, production-ready ML/GenAI solutions Responsibilities: • Design and implement ...

VAST Data

Senior Software Engineer - Database

Manhattan, NY · On-site +1

$116K - $158K/yr

Proven track record of backend work on high-throughput databases, vector stores, or real-time processing engines * Bachelor's degree in Computer Science, Engineering, or equivalent experience Nice to ...

VAST Data

Senior Software Engineer - Database

Manhattan, NY · On-site +1

$116K - $158K/yr

Proven track record of backend work on high-throughput databases, vector stores, or real-time processing engines * Bachelor's degree in Computer Science, Engineering, or equivalent experience Nice to ...

Diverse Lynx

AI/ML Engineer

Iselin, NJ

... vector databases • Experience with agentic frameworks (e.g., LangChain, LangGraph, AutoGen) • Strong system design skills with experience building and scaling cloud-based applications Roles ...

Merican

Context Engineer (Model Risk Focus)

New York, NY · On-site

$50 - $53/hr

Work with vector databases and embedding techniques to enable retrieval augmented context. * Partner with AI engineers and architects to integrate context layers into agentic systems. Required Skills

Arohak Inc

Gen AI Engineer

Raritan, NJ

... vector databases and embedding strategies for semantic search Implement guardrails, security measures, and bias mitigation techniques Monitor model performance, latency, and cost optimization ...

TMS

AI/ ML Engineer

New York, NY · Remote

$60 - $62/hr

Work with vector databases for embeddings and semantic search * Collaborate with product, data, and engineering teams to translate business needs into AI solutions * Optimize model performance ...

Merican

AI Architect - Model Lifecycle

New York, NY · On-site

$57 - $60/hr

Design scalable architecture using vector databases, embeddings, and workflow orchestration. * Work with engineering teams to integrate AI components into enterprise systems. * Provide technical ...

UnivEdge Consulting LLC

AI Architect

Manhattan, NY

Design and architect AI-powered applications using LLMs, RAG, Vector Databases, and Agentic AI frameworks. * Build scalable multi-agent orchestration solutions and intelligent workflows. * Define AI ...

Photon

Sr Gen AI Engineer - NY/NJ

Manhattan, NY · On-site

PostgreSQL, Vector Databases, and Advanced Retrieval strategies. ML/DL: PyTorch, TensorFlow, and Model Fine-tuning. Deployment: Docker, Production API management, and LLM monitoring. Tools: Prompt ...

Photon

AI Engineer - NY

Manhattan, NY · On-site

Integrate agents with vector databases, RAG pipelines, and knowledge graphs. Production AI Systems * Implement observability, evaluation, and guardrails for agent behavior. * Optimize AI pipelines ...

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Vector Databases information

What are vector databases?

Vector databases are specialized databases designed to store, manage, and search high-dimensional vector data, which is commonly generated from machine learning models, such as embeddings from natural language processing or image recognition. They enable efficient similarity search operations, such as finding the most similar items to a given query vector, which is essential for applications like recommendation systems, semantic search, and AI-powered search engines. Unlike traditional databases that handle structured or unstructured data, vector databases are optimized for fast and scalable similarity searches on large datasets of vectors.

What are some common challenges faced when working with vector databases, and how can they be addressed?

Professionals working with vector databases often encounter challenges such as efficiently scaling to handle large datasets, ensuring low-latency similarity searches, and integrating the database with machine learning pipelines. To address these, teams typically implement distributed architectures, fine-tune indexing strategies, and collaborate closely with data engineers and machine learning specialists. Staying updated with the latest developments in vector database technologies and maintaining clear communication with cross-functional teams are also key to overcoming these challenges.

What is the difference between Vector Databases vs Data Engineers?

AspectVector DatabasesData Engineers
Required SkillsDatabase management, data modeling, query optimizationData pipeline development, ETL processes, programming
Work EnvironmentData storage systems, AI/ML projects, cloud platformsData infrastructure, cloud environments, big data tools
Industry UsageAI, machine learning, recommendation systemsData integration, analytics, data architecture

While Vector Databases focus on storing and querying high-dimensional vector data for AI applications, Data Engineers build and maintain data pipelines and infrastructure to support data analysis and machine learning workflows. Both roles are essential in data-driven industries but serve different functions within the data ecosystem.

What are the key skills and qualifications needed to thrive as a Vector Database Engineer, and why are they important?

Success as a Vector Database Engineer requires a strong background in computer science, database management, and experience with machine learning or AI-driven data systems. Familiarity with vector database platforms (such as Pinecone, Milvus, or Weaviate), cloud infrastructure, and proficiency in languages like Python are typically expected. Strong problem-solving skills, effective communication, and the ability to work cross-functionally help engineers stand out. These competencies are vital to efficiently design, deploy, and maintain scalable vector search solutions that power modern AI applications.
What are popular job titles related to Vector Databases jobs in New York? For Vector Databases jobs in New York, the most frequently searched job titles are:
What job categories do people searching Vector Databases jobs in New York look for? The top searched job categories for Vector Databases jobs in New York are:
What cities in New York are hiring for Vector Databases jobs? Cities in New York with the most Vector Databases job openings:
Vector Databases jobs near you
Infographic showing various Vector Databases job openings in New York as of June 2026, with employment types broken down into 86% Full Time, 8% Part Time, 3% Temporary, and 3% Contract. Highlights an 66% Physical, 4% Hybrid, and 30% Remote job distribution.

Agentic AI Developer (Python) Vertex AI RAG + Graph/Vector Datastores

Envision Technology Solutions

Berkeley Heights, NJ • Hybrid

$52.50 - $72.25/hr

Other

Posted 18 days ago

Job description

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).

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