Vector Database (DB) Engineer
3B Staffing LLC, New York, NY, United States
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
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