Vector Database Engineer
9 hours ago
Description:
We are seeking a Postgres Database Engineer with expertise in maintaining Postgres databases optimized for vector search using pgvector. You will manage performance, availability, backup, and DR for AI-driven enterprise workloads.
Responsibilities:
- Maintain Postgres with pgvector extension for embeddings.
- Optimize vector similarity search queries at scale.
- Implement backups, restore, and DR drills.
- Monitor performance metrics and replication lag.
- Collaborate with AI/ML teams for retrieval optimization.
Qualifications:
- Bachelors in Computer Science, Data Engineering, or related field.
- 4+ years DBA experience with Postgres.
- Skilled in query tuning, partitioning, indexing.
- Experience with pgBackRest/WAL-G.
- Familiarity with Postgres HA setups.
Required Skills
- Vector database design, deployment, and optimization.
- Familiarity with platforms: Milvus, Pinecone, Chroma, pgvector/PostgreSQL, MongoDB Atlas Vector Search.
- Advanced proficiency in Approximate Nearest Neighbor (ANN) algorithms, e.g., HNSW, IVF, PQ, and LSH, for low-latency vector search at scale
- Building and tuning hybrid indexes to support both vector similarity and metadata (structured) filtering in queries.
- Handling sub-100ms response times for high-throughput, real-time search and recommendation workloads
- Embedding generation and manipulation: Skilled at generating semantic embeddings using models like BERT, Word2Vec, Sentence-BERT, and integrating these with database workflows.
- Vector-aware sharding, data partitioning, and rebalancing strategies for scaling and resilience
- Storage optimization including vector quantization, sparse data representations, and time-based partitioning for hot/cold data separation.
Career Growth:
Pathway to Database Architect or Data Platform Specialist roles focused on enterprise-scale AI workloads.
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