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Qdrant Jobs in New York (NOW HIRING)

Virtusa Corporation

Senior Architect

New York, NY · On-site

Technical Skillset Python, FastAPI OpenAI, Azure OpenAI, Anthropic Claude, Google Gemini LangGraph, LangChain, LlamaIndex, CrewAI Pinecone, Qdrant, Weaviate, ChromaDB PostgreSQL, Redis, Neo4j Docker ...

Precision Technologies Corp

Python GenAI

Jersey City, NJ · On-site

$52.50 - $72.25/hr

Enterprise Redis Database, Qdrant, Cockroach * Must have experience of working with Model Risk Management/Model Governance, which includes * Delivering on a Gen AI project following the model ...

Lynx Analytics

AI Engineer (US)

New York, NY · On-site

$114K - $157K/yr

Practical experience with GraphRAG or knowledge graph-based retrieval (e.g., Neo4j, Microsoft GraphRAG) and vector databases (Pinecone, Weaviate, Qdrant, etc.). * Proficiency in Python and solid ...

Tavily

Forward Deployed Engineer, Enterprise

Manhattan, NY · On-site

$179.50 - $224.30/hr

Experience with vector databases such as Pinecone, Weaviate, pgvector, Qdrant, or similar systems. * Experience with enterprise AI use cases in financial services, legal, consulting, enterprise SaaS ...

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Qdrant information

What is the difference between Qdrant vs Data Scientist?

AspectQdrantData Scientist
Required CredentialsTechnical certifications, knowledge of vector databasesDegree in Data Science, Statistics, or related field
Work EnvironmentTech companies, startups, AI-focused firmsResearch labs, tech companies, consulting firms
Industry UsageAI, machine learning, data storageData analysis, predictive modeling, research

Qdrant primarily focuses on managing and deploying vector similarity search databases, requiring technical skills in database management and AI tools. Data Scientists analyze data, build models, and interpret results. While both roles operate within the tech and AI industry, Qdrant specialists are more technical and infrastructure-oriented, whereas Data Scientists focus on data analysis and modeling.

What are popular job titles related to Qdrant jobs in New York?

For Qdrant jobs in New York, the most frequently searched job titles are:

What job categories do people searching Qdrant jobs in New York look for?

The top searched job categories for Qdrant jobs in New York are:

What cities in New York are hiring for Qdrant jobs?

Cities in New York with the most Qdrant job openings:

Qdrant jobs near you
Infographic showing various Qdrant job openings in New York as of August 2026, with employment types broken down into 94% Full Time, 1% Part Time, and 5% Contract. Highlights an 68% Physical, 4% Hybrid, and 28% Remote job distribution.

Vector Database (DB) Engineer

3B Staffing LLC

Manhattan, NY • Hybrid

Full-time

Posted 2 days ago

New

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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