Resume & Job Matching
with Qdrant Semantic & Hybrid Search
AI-powered talent matching platform using Qdrant vector database, achieving 10x faster candidate shortlisting and 30-40% better match quality through semantic and hybrid search capabilities.
Background
An enterprise staffing and recruitment platform was struggling with traditional keyword-based resume search systems that failed to understand semantic meaning and context. With over 100,000 resumes in their database, recruiters spent hours manually reviewing candidates, often missing qualified candidates due to variations in terminology and skill descriptions.
The existing system couldn't match "Spring Boot" with "Java backend framework" or understand that a candidate with "AWS Lambda" experience likely has serverless architecture skills. This resulted in poor match quality, frustrated recruiters, and missed opportunities for both candidates and clients.
The client needed an AI-powered semantic search solution that could understand meaning beyond keywords, combine vector similarity with metadata filtering, and provide explainable matching results at scale.
Key Challenges
Traditional keyword search couldn't handle the complexity of modern talent matching
Keyword Limitations
Traditional keyword search failed to match semantically similar skills with different wording (e.g., 'Spring Boot' vs 'Java backend framework'), resulting in missed qualified candidates.
Scale & Performance
Needed to search across 100K+ resumes with sub-second response times while maintaining high relevance and accuracy in matching.
Hybrid Search Requirements
Recruiters needed to combine semantic meaning with strict filters (experience level, location, education) for precise candidate shortlisting.
Relevance Ranking
No intelligent ranking system to prioritize candidates by relevance, experience, and contextual fit beyond simple keyword frequency.
The Qdrant Solution
AI-powered semantic and hybrid search with vector database technology
Semantic Search
Meaning-based resume matching using vector embeddings to understand skill relationships beyond keywords
Hybrid Search
Combines semantic relevance with metadata filtering for precise candidate shortlisting based on experience, location, and skills
Qdrant Vector Database
High-performance vector storage with payload filtering, enabling fast semantic search at scale with metadata constraints
OpenAI Embeddings
Generate high-quality vector representations of resumes and job descriptions for accurate semantic matching
Intelligent Ranking
Score candidates by semantic similarity, metadata alignment, and contextual fit for optimal relevance ordering
Structured Data Storage
Store resume embeddings alongside metadata (skills, experience, location) for powerful hybrid filtering
Qdrant Data Schema
Structured storage combining vector embeddings with rich metadata for hybrid search
| Field | Type | Description |
|---|---|---|
| resume_embedding | Vector | 768-dim OpenAI embedding |
| candidate_name | Payload | Full name |
| skills | Payload | Array of skills |
| years_experience | Payload | Total experience |
| location | Payload | Geographic location |
| education | Payload | Degree information |
Example Hybrid Search Query
- Experience ≥ 5 years
- Location = USA
- Must include "Spring Boot"
Business Impact & Results
Measurable improvements in recruiter efficiency and candidate matching quality
Faster Candidate Shortlisting
Reduced time to shortlist from hours to minutes
Better Match Quality
Improved semantic relevance and candidate-job fit
Resumes Processed
Successfully indexed and searchable at scale
Search Response Time
Sub-second semantic + hybrid search results
Reduced Recruiter Effort
Less manual screening with better initial matches
Match Accuracy
High-quality semantic understanding of skills
Technical Architecture
Four-layer architecture for scalable semantic search
Frontend Layer
Modern web interface for resume upload and semantic search
Backend API Layer
API services for embedding generation and search orchestration
AI Embedding Layer
Generate semantic embeddings for resumes and job descriptions
Vector Database Layer
High-performance vector storage and semantic search engine
Strategic Business Impact
Transforming talent acquisition through AI-powered semantic search
Recruiter Productivity
10x faster candidate shortlisting enabled recruiters to fill positions faster and handle more requisitions simultaneously.
Placement Quality
30-40% improvement in match quality resulted in better candidate-job fit, reducing early turnover and improving client satisfaction.
Candidate Experience
Better matches meant candidates were approached for more relevant opportunities, improving engagement and acceptance rates.
Competitive Advantage
AI-powered semantic search became a key differentiator, attracting both clients seeking better talent and candidates seeking relevant opportunities.
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