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Vector Databases Jobs in New York, NY (NOW HIRING)

3B Staffing LLC

Senior AI ML Engineer

Iselin, NJ · On-site

$106K - $145K/yr

Proven experience with RAG architectures, embeddings, and vector databases * Experience with agentic frameworks (e.g., LangChain, LangGraph, AutoGen) * Strong system design skills with experience ...

3B Staffing LLC

Senior AI ML Engineer

Iselin, NJ · On-site

$106K - $145K/yr

Proven experience with RAG architectures, embeddings, and vector databases * Experience with agentic frameworks (e.g., LangChain, LangGraph, AutoGen) * Strong system design skills with experience ...

3B Staffing LLC

Data Analytics Engineer (AI)

Stamford, CT · On-site

$122K - $146K/yr

Develop and manage prompt engineering frameworks, embeddings, and vector databases * Translate business requirements into AI-powered workflows and intelligent automation solutions AI Implementation ...

3B Staffing LLC

Data Analytics Engineer (AI)

Stamford, CT · On-site

$122K - $146K/yr

Develop and manage prompt engineering frameworks, embeddings, and vector databases * Translate business requirements into AI-powered workflows and intelligent automation solutions AI Implementation ...

Northern Base

Gen AI Copilot Lead

Edison, NJ · On-site

Vector Databases * LLM Evaluation & Optimization * Python / C# / JavaScript Development * REST APIs & Enterprise Integration Patterns * Azure Cloud Architecture * AI Solution Architecture & Design

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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 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 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 popular job titles related to Vector Databases jobs in New York, NY?

For Vector Databases jobs in New York, NY, the most frequently searched job titles are:

What job categories do people searching Vector Databases jobs in New York, NY look for?

The top searched job categories for Vector Databases jobs in New York, NY are:

What cities near New York, NY are hiring for Vector Databases jobs?

Cities near New York, NY with the most Vector Databases job openings:

Vector Databases jobs near you
Infographic showing various Vector Databases job openings in New York, NY as of August 2026, with employment types broken down into 63% Full Time, and 37% Contract. Highlights an 100% In-person job distribution.

Vector Database (DB) Engineer

3B Staffing LLC

Manhattan, NY • Hybrid

Full-time

This job post has expired today. Applications are no longer accepted.

Job description

Job Role: Vector Database (DB) Engineer

Location: 55 Hudson Yards, NYC

Work Schedule: 3 days a week in the office and 2 days remote.

VISA status. US citizens, Green Card, H4-EAD,

Vector Database Engineer

Description:

  • Design, develop, and maintain high-performance vector database systems (e.g., Pinecone, Weaviate, Milvus, FAISS, Qdrant) for LLM-backed applications.
  • Integrate LLMs (OpenAI, Claude, LLaMA, etc.) into data pipelines and AI solutions using RAG and embedding-based retrieval.
  • Build and manage embedding pipelines (using OpenAI, HuggingFace, SentenceTransformers, etc.) for structured and unstructured data.
  • Optimize vector search for latency, relevance, and scalability across large datasets.
  • Collaborate with ML/AI engineers, data scientists, and product teams to deliver end-to-end solutions powered by AI.
  • Monitor performance, ensure data security, and maintain high system availability.
  • Evaluate and experiment with different vector indexing techniques (e.g., HNSW, IVF, PQ) and distance metrics (cosine, Euclidean, dot-product).
  • Stay updated with the latest advancements in LLMs, vector databases, and semantic search.

Comparison of Top Vector Databases: Key Points and Use Cases

Database

Key Features

Use Cases

Chroma

LangChain integration, modular codebase, various storage options for vector embeddings

LLM applications, NLP

Pinecone

Seamless API, metadata filters, high-performance search and similarity matching

AI solutions, large datasets

Deep Lake

Data streaming, querying, integration with tools like LlamaIndex and LangChain

LLM-based applications, deep learning

Vespa

Redundancy configuration, flexible query options, efficient similarity searches

Data organization, large-scale search

Milvus

Simple unstructured data management, scalable, supported by community

Chatbots, image search, chemical structure

ScaNN

Search space trimming, quantization, balance of efficiency and accuracy

Vector similarity search at scale

Weaviate

AI-powered searches, MLOps integration, Kubernetes compatibility

Text, image, and data vectorization

Qdrant

Extensive filtering support, independent orchestration, cached payload information

Semantic-based matching, neural networks

Vald

Index backup, vector indexing, horizontal scaling, adaptable configuration

Fast, distributed vector search

Faiss

Fast dense vector similarity search, multiple distances supported, efficient vector grouping

Large-scale vector search, clustering

OpenSearch

Combines vector search with analytics, supports semantic and multimodal search

AI applications, personalization, data quality

Pgvector

PostgreSQL extension, supports inner product and cosine distance, embedding storage

Exact and approximate nearest neighbor search

Apache Cassandra

SAI framework, ANN search capabilities, high-dimensional vector storage

Big data handling, high availability

Elasticsearch

Distributed architecture, automatic node recovery, high availability, clustering

Data analytics, large-scale search

ClickHouse

Data compression, robust SQL support, multi-server and multi-core setup

Real-time analytical reports, large queries

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