What Vector Database Engineers Actually Build
Vector databases enable AI data applications. Understanding what engineers build helps you hire effectively:
RAG Infrastructure
The foundation for AI knowledge systems:
- Document search - Semantic search over enterprise documents
- Knowledge retrieval - Finding relevant context for LLMs
- Question answering - Powering AI assistants with company data
Examples: Every production RAG system needs vector storage
Semantic Search Systems
Beyond keyword matching:
- Product search - "Comfortable running shoes" finds relevant items
- Content discovery - Recommend similar articles or videos
- Code search - Find similar code patterns across repositories
Examples: E-commerce search, media platforms, developer tools
Recommendation Systems
Personalization at scale:
- Content recommendations - "Users like you also liked..."
- Product recommendations - Similar items, complementary products
- Personalized feeds - Custom content ranking
Examples: Streaming platforms, e-commerce, social media
Anomaly Detection & Similarity
Finding patterns in data:
- Fraud detection - Identifying unusual patterns
- Image similarity - Finding visually similar items
- Duplicate detection - Finding near-duplicates at scale
Understanding Vector Databases
How They Work
Vector databases solve a specific problem:
- Data → Embeddings - Convert text/images to vectors (numbers)
- Store - Index vectors for efficient retrieval
- Query - Find similar vectors using distance metrics
- Return - Retrieve the most relevant results
Key Concepts for Hiring
When interviewing, these terms matter:
- Embeddings - Numerical representations of data
- Similarity metrics - Cosine, Euclidean, dot product
- ANN (Approximate Nearest Neighbor) - Fast similarity search algorithms
- Indexing - HNSW, IVF, and other index types
- Hybrid search - Combining vector and keyword search
The Landscape
Different tools for different needs:
- Pinecone - Managed, simple, reliable, enterprise-focused
- Weaviate - Open-source, GraphQL API, rich features
- Chroma - Developer-friendly, easy to start, good for prototyping
- Milvus - Scalable, open-source, for large deployments
- Qdrant - High performance, written in Rust
- pgvector - PostgreSQL extension, familiar operations model
The Vector Database Engineer Profile
They Understand Embeddings
Strong vector DB engineers know:
- Embedding models - OpenAI, Cohere, sentence-transformers
- Dimensionality - Trade-offs of different vector sizes
- Quality evaluation - How to measure embedding quality
- Model selection - Choosing the right embedding model
They Think About Scale
AI data grows fast:
- Indexing strategies - Choosing the right index type
- Sharding and partitioning - Distributing data
- Performance tuning - Optimizing query latency
- Cost management - Vectors are expensive to store
They Bridge AI and Infrastructure
Vector DB engineers work at the intersection:
- AI workflows - Understanding RAG, search, recommendations
- Data engineering - ETL pipelines, data quality
- Infrastructure - Deployment, scaling, monitoring
- Backend development - API design, integration
Skills Assessment by Project Type
For RAG Applications
- Priority: Embedding selection, chunking strategies, retrieval optimization
- Interview signal: "How would you build vector search for 1M documents?"
- Red flag: Only knows basic similarity search
For Semantic Search
- Priority: Hybrid search, ranking, relevance tuning
- Interview signal: "How would you combine vector and keyword search?"
- Red flag: Doesn't understand keyword search limitations or when to use hybrid
For Scale/Infrastructure
- Priority: Performance optimization, sharding, cost management
- Interview signal: "How would you handle 100M vectors with sub-100ms latency?"
- Red flag: No experience with scale or performance tuning
Common Hiring Mistakes
1. Conflating Vector DB with General Database Work
Vector databases are specialized:
- Different indexing algorithms
- Different query patterns
- Different optimization strategies
- Requires embedding knowledge
Traditional database experience helps but isn't sufficient.
2. Over-Focusing on Specific Tools
Pinecone, Weaviate, Chroma—the concepts transfer:
- Embedding and similarity fundamentals
- Indexing and retrieval patterns
- Integration with AI systems
A developer strong in one can learn another quickly.
3. Ignoring the AI Context
Vector databases serve AI applications:
- Understanding RAG and how retrieval fits
- Knowledge of embedding models
- Integration with LLM workflows
Hire for AI context, not just database skills.
4. Underestimating Data Engineering
Vector DB work involves significant data work:
- Ingestion pipelines
- Embedding generation at scale
- Data quality and updates
- Metadata management
Recruiter's Cheat Sheet
Questions That Reveal Expertise
| Question | Junior Answer | Senior Answer |
|---|---|---|
| "How do you choose an embedding model?" | "Use OpenAI embeddings" | Discusses task fit, dimension trade-offs, cost, benchmark evaluation, domain-specific options |
| "What's HNSW?" | "A type of index" | Explains graph-based ANN, trade-offs (memory vs speed), when to use it vs IVF, parameter tuning |
| "How do you handle updates?" | "Just update the vectors" | Discusses re-embedding triggers, stale data handling, incremental vs full reindexing, consistency patterns |
Resume Green Flags
- Production vector database deployments
- Scale metrics (vector count, QPS, latency)
- Experience with multiple vector DBs
- Integration with RAG or search systems
- Mentions embedding model selection
Resume Red Flags
- Only tutorial-level projects
- No production deployment
- Doesn't mention embeddings
- Only used one vector database
- No understanding of scale considerations