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Vector Databases Jobs in Irvington, NJ (NOW HIRING)

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 ...

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

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

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 ...

BIGDATAAPPTECH LLC

Gen AI Architect

Jersey City, NJ · On-site

RAG Pipelines, Vector Databases & MCP Architect and deploy RAG pipelines using vector databases such as: Pinecone Weaviate ChromaDB FAISS Implement MCP Servers and Agent-to-Agent (A2A) communication ...

Photon

Python + Gen AI Developer - New York

Manhattan, NY · On-site

$55 - $76/hr

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 ...

Accord Technologies Inc.

Senior AI Engineer

Piscataway, NJ · On-site

$106K - $146K/yr

Design and implement solutions involving Large Language Models (LLMs), embeddings, vector databases, Retrieval-Augmented Generation (RAG), and prompt engineering. * Work with cloud AI services such ...

Tekcel8

Gen AI

Manhattan, NY · On-site

The ideal candidate will have hands-on experience with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, prompt engineering, vector databases, and cloud-native AI ...

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All JobsVector Databases JobsVector Databases Jobs in Irvington, NJ

Vector Databases information

What is the salary of a vector database developer?

The salary of a vector database developer typically ranges from $80,000 to $150,000 annually, depending on experience, location, and company size. Skilled developers with expertise in machine learning, data structures, and database management may earn higher salaries, especially in tech hubs or with advanced certifications.

Are vector databases the future?

Vector database jobs involve managing and optimizing databases designed for high-dimensional vector data, which are essential for AI and machine learning applications. As AI continues to grow, demand for professionals skilled in vector database technologies and related tools like embedding models is expected to increase, making this a promising field for future job opportunities.

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 can you do with a vector database?

A vector database is used in roles involving data management and machine learning to store, search, and retrieve high-dimensional vector representations of data such as images, text, or audio. It enables efficient similarity searches, supporting applications like recommendation systems, natural language processing, and computer vision. Working with a vector database often requires knowledge of data structures, indexing techniques, and programming skills in languages like Python or C++.

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 the top 5 vector databases?

Top vector databases used in data management and AI applications include Pinecone, Weaviate, FAISS, Milvus, and Annoy. These databases are optimized for storing and searching high-dimensional vector data, often requiring skills in machine learning and database management. They are widely adopted for tasks like similarity search and recommendation systems.
What are popular job titles related to Vector Databases jobs in Irvington, NJ? For Vector Databases jobs in Irvington, NJ, the most frequently searched job titles are:
What job categories do people searching Vector Databases jobs in Irvington, NJ look for? The top searched job categories for Vector Databases jobs in Irvington, NJ are:
What cities near Irvington, NJ are hiring for Vector Databases jobs? Cities near Irvington, NJ with the most Vector Databases job openings:
Vector Databases jobs near you
Infographic showing various Vector Databases job openings in Irvington, NJ as of July 2026, with employment types broken down into 74% Full Time, and 26% Contract. Highlights an 90% In-person, 5% Hybrid, and 5% Remote job distribution.
Agentic AI Lead with Vertex AI and Vector DB

Agentic AI Lead with Vertex AI and Vector DB

Alltech Consulting Services, Inc.

Berkeley Heights, NJ • On-site

Other

Posted 2 days ago

Job description

Job Title: Agentic AI Lead with Vertex AI and Vector DB

Location: : Berkeley Heights NJ (ONSITE)

CONTRACT W2 or FTE

JOB 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.

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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