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Vector Databases Jobs in New York (NOW HIRING)

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 ...

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 ...

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 ...

ApTask

Enterprise Architect - Generative AI

Manhattan, NY · On-site

$76 - $98/hr

Responsibilities : • Lead enterprise-wide Generative AI architecture strategy and roadmap initiatives. • Design and implement scalable AI solutions using LLMs, RAG frameworks, vector databases ...

Clearview AI, Inc.

Senior Software Engineer

New York, NY

$134K - $176K/yr

You will build pipelines that ingest petabyte-scale data into object storage and turn it into fast, queryable databases and vector stores, design large-scale storage and retrieval across hot and cold ...

Hirekeyz Inc

Agentic AI Developer

Berkeley Heights, NJ · On-site

... 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 ...

MphasiS Corporation USA

Agentic AI Lead

Berkeley Heights, NJ · On-site

$146K - $179K/yr

... 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 ...

SysMind Tech

Gen-AI Engineers

Jersey City, NJ · On-site

$65 - $70/hr

Integrate Gen-AI solutions with enterprise systems, APIs, databases, and cloud platforms. * Develop and optimize prompts, embeddings, vector search, and RAG-based architectures. * Collaborate with ...

Clearview AI

Senior Software Engineer

Manhattan, NY · On-site

$175K - $220K/yr

You will build pipelines that ingest petabyte-scale data into object storage and turn it into fast, queryable databases and vector stores, design large-scale storage and retrieval across hot and cold ...

GRP SCAN Group

Principal AI Architect

Long Beach, NY · On-site

$147.90 - $254.80/hr

Oversee vector database design (Azure AI Search or equivalent) and integration with Snowflake/Fabric data hubs. Implement high‑availability, cost‑optimized compute and storage strategies for AI ...

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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 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 July 2026, with employment types broken down into 66% Full Time, and 34% Contract. Highlights an 89% In-person, and 11% Remote job distribution.
Technology Development Manager / Architect - New York

Technology Development Manager / Architect - New York

Photon

Manhattan, NY • On-site

Other

Medical, Dental, Vision, Retirement, PTO

Re-posted 8 days ago

Job description

Job Title: Generative AI Technical Lead Overview

We are seeking a Hands-on Generative AI Technical Lead to design, build, and scale AI-powered applications using state-of-the-art large language models (LLMs) and multimodal systems. This role combines deep technical expertise with leadership, requiring active coding, architecture ownership, and mentorship of engineering teams.

Key Responsibilities
  • Lead end-to-end GenAI development
    • Design, develop, and deploy LLM-based applications (chatbots, copilots, agents, RAG systems)
    • Build scalable, production-grade AI systems
  • Hands-on engineering
    • Write high-quality code (Python, APIs, pipelines)
    • Implement prompt engineering, fine-tuning, embeddings, and vector search
    • Work directly with frameworks like LangChain, LlamaIndex, or similar
  • Architecture & system design
    • Define GenAI architecture (RAG, agents, tool use, orchestration)
    • Optimize performance, latency, and cost of AI systems
  • Model integration
    • Integrate with OpenAI, Anthropic, open-source models (Llama, Mistral, etc.)
    • Evaluate and benchmark models for use cases
  • Data & retrieval systems
    • Design vector databases (Pinecone, Weaviate, FAISS, etc.)
    • Build ingestion pipelines and knowledge retrieval systems
  • Team leadership
    • Mentor engineers and guide best practices
    • Conduct code reviews and technical design reviews
  • Experimentation & innovation
    • Stay current with GenAI trends (agents, multimodal, fine-tuning, evals)
    • Rapidly prototype and validate new ideas
  • AI governance & safety
    • Implement guardrails, monitoring, and evaluation frameworks
    • Ensure responsible and secure AI usage
Required Skills & Qualifications
  • 10+ years of software engineering experience
  • Strong programming skills in Python
  • Experience with:
    • LLM APIs (OpenAI, Anthropic, etc.)
    • RAG pipelines and vector databases
    • Prompt engineering and evaluation techniques
  • Solid understanding of:
    • NLP concepts, embeddings, transformers
    • Distributed systems and cloud platforms (AWS/GCP/Azure)
  • Experience building and deploying APIs and microservices

  • Compensation, Benefits and Duration

    Minimum Compensation: USD 62,000
    Maximum Compensation: USD 217,000
    Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role.
    Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full time employees.
    This position is not available for independent contractors
    No applications will be considered if received more than 120 days after the date of this post

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