Building a Semantic Talent Matching System with Vector Search
How we reduced time-to-source by 90% using vector databases and LLM embeddings for intelligent candidate discovery.
The Challenge: Manual Talent Sourcing at Scale
Traditional keyword-based searches were failing to identify high-quality candidates already in the system. Recruiters spent hours manually searching through databases, often missing perfect matches due to terminology mismatches or incomplete search criteria.
Key Pain Points:
- Average 4-6 hours per role for initial candidate sourcing
- 70% of high-fit candidates missed due to keyword limitations
- Inconsistent search quality across different recruiters
Technical Implementation
We built an internal sourcing tool leveraging modern vector search technology combined with LLM-powered re-ranking for superior candidate matching.
Architecture Components
Vector Database Layer
Technology: Qdrant
Stores candidate profiles as high-dimensional vectors, enabling semantic similarity search beyond keyword matching.
Embedding Generation
Technology: OpenAI Embeddings API
Converts both job descriptions and candidate profiles into dense vector representations that capture semantic meaning.
Intelligent Re-ranking
Technology: Claude API
Post-processes initial vector search results to assess nuanced fit factors and provide qualitative match explanations.
API Layer
Technology: FastAPI
High-performance Python backend handling embedding generation, vector search, and LLM orchestration.
Implementation Workflow
1. Profile Ingestion └── Structured and unstructured candidate data processed into unified representations 2. Embedding Pipeline └── Real-time generation of embeddings for new profiles and job descriptions 3. Semantic Matching └── Vector similarity search returns initial candidate set based on meaning, not keywords 4. LLM Enhancement └── Claude analyzes matches for soft skills, cultural fit, and hidden qualifications 5. Result Delivery └── Top 25 matches presented with match rationale in seconds
Measurable Impact
Recruiters now focus on high-value activities like candidate engagement and assessment rather than manual searching. The system has fundamentally changed how talent acquisition operates, making it more strategic and less operational.
Key Technical Learnings
Embedding Model Selection
We tested multiple embedding models and found OpenAI's ada-002 provided the best balance of semantic understanding and latency for our use case.
Vector Index Optimization
HNSW index configuration in Qdrant was crucial for sub-100ms search performance at our scale of 500K+ profiles.
Hybrid Search Strategy
Combining vector similarity with traditional filters (location, experience level) yielded better results than pure semantic search.
LLM Cost Management
Implementing intelligent caching and batch processing reduced Claude API costs by 75% without impacting user experience.
Next Steps
- Fine-tuning custom embedding models on recruitment-specific vocabulary
- Implementing feedback loops to continuously improve match quality
- Expanding to include skills inference from project descriptions
- Building automated outreach suggestions based on candidate preferences
Interested in implementing semantic search?
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