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Vector Databases Jobs in Queens, NY (NOW HIRING)

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 ...

Virtusa Corporation

Vectorization Expert

New York, NY · On-site

Manage and optimize vector databases (Pinecone, Weaviate, Milvus, pgvector), including building efficient indexing strategies (HNSW, DiskANN). Develop advanced chunking and parsing techniques to ...

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 ...

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 ...

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 ...

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 ...

VRN Technologies

AI Native Engineer

Jersey City, NJ · On-site

Develop Retrieval-Augmented Generation (RAG) systems using vector databases and semantic search. * API/SDK Integration: Integrate LLMs (OpenAI, Anthropic) into applications using function calling ...

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 ...

Technodeed LLC

AI Platform Engineer

Manhattan, NY · On-site

GPT, Claude, Gemini, LLaMA ✔ Agentic AI, MCP, Graph/RAG, LLM Pipelines ✔ IAM, Security Controls, Logging & Compliance ✔ Terraform, Docker, Infrastructure as Code ✔ Vector Databases (PGVector ...

OuterSignal Inc

Senior Software Engineer

New York, NY · On-site

$200K - $300K/yr

Integrate vector databases + RAG pipelines to make customer profiles smarter, faster, and searchable in real time * Ship features end-to-end: APIs, dashboards, integrations (Shopify, Klaviyo, Slack ...

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

Python + Gen AI Developer - New York

Manhattan, NY

$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 ...

Clearview AI

Senior Software Engineer

Manhattan, NY · On-site

$175 - $220/hr

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 ...

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Showing results 21-40

All JobsVector Databases JobsVector Databases Jobs in Queens, NY

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 Queens, NY?

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

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

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

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

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

Vector Databases jobs near you

Member of Technical Staff (Software Engineer, Storage Platform)

Perplexity AI

Manhattan, NY • On-site

Other

Posted 2 days ago

New

Job description

About the Role
The Storage Platform team owns the infrastructure that powers how Perplexity persists, retrieves, and manages data across all systems, ensuring high availability, performance, and cost-efficiency for every product and AI workload.
This foundational, high-impact group is responsible for the full spectrum of storage technologies - relational databases, vector databases, custom storage implementations, and the tooling to provision, monitor, and optimize them at scale.
By building robust abstractions and operational excellence around storage, the team enables engineers across Perplexity to focus on product innovation while trusting that data is fast, reliable, and always accessible.
Key Responsibilities
  • Design, operate, and continuously improve the storage platform that underpins all Perplexity products and AI workloads, including relational, NoSQL, and vector databases.
  • Own reliability, performance, and availability SLOs for stateful systems, driving root cause analysis and long-term fixes for storage-related incidents.
  • Define and evolve opinionated storage abstractions, APIs, and tooling that make it simple and safe for product teams to consume storage at scale.
  • Partner with AI, infra, and product teams to design data models and access patterns that meet low-latency, high-throughput, and cost-efficiency requirements.
  • Lead capacity planning, benchmarking, and lifecycle management for storage clusters, including scaling, migrations, and deprecations.

Qualifications
  • 5+ years of experience building and operating large-scale backend or infrastructure systems in production.
  • Deep hands-on experience with at least one major database technology (for example, Postgres, MySQL, DynamoDB, Cassandra) and familiarity with modern cloud storage services.
  • Strong fundamentals in distributed systems, data modeling, and performance optimization for high-throughput, low-latency workloads.
  • Proven track record of owning services end-to-end, including design, implementation, deployment, and on-call.
  • Proficiency in at least one programming language used for backend systems (such as Go, Rust, or Python).
  • Effective communication and collaboration skills, with the ability to partner closely with product, infra, and AI teams.

If you're excited about this role, we encourage you to apply even if your experience doesn't match every qualification listed above.

Apply