JobMatcher AI is a semantic search job matching platform that combines Hybrid Similarity(Heuristic Score and Semantic Similarity) with Transformer models to recommend the best-matching jobs tailored to a candidate's key skill set, experience and domain expertise using Mantiks API + Pinecone Vector DB for Vector Store and Embedding + Hugging Face for Model Deployment.
Traditional job search platforms often require job seekers to manually scroll through dozens — sometimes hundreds — of job descriptions, line by line, to evaluate whether their skills and experiences truly align with each opportunity. While keyword-based search engines may return roles with similar titles, they frequently fail to capture deeper alignment:
Do the specific frameworks, tools, or domain expertise match the candidate’s background?
Does the job use a tech stack or architecture that the applicant is confident in?
Are subtle industry requirements being overlooked by a generic search?
This fragmented experience leads to wasted time, decision fatigue, and often, missed opportunities. JobMatcher AI was born from the idea that we can solve this pain point with the help of machine learning. Instead of asking job seekers to evaluate every job one by one, why not train a model to do it for them — instantly and intelligently?
JobMatcher AI leverages state-of-the-art semantic search models supported by heuristic signals(Skills Matching and Experience Allignment) to deeply analyze both resumes and job descriptions. Within seconds, it returns the top-matching jobs, not just by title, but by meaningful skill and context alignment.
JobMatcher AI solves this through a state-of-the-art semantic search model supported by a hybrid scoring system that combines:
- Semantic Understanding: Deep contextual analysis using transformer models
- Heuristic Signals:
- Skill Matching: Precise extraction and comparison of technical requirements
- Experience Alignment: Years of experience validation against job requirements
- Weighted Re-ranking: Intelligent combination of multiple signals for accurate prioritization
- Feedback labelled Fine-tuning: Model trained and fine-tuned using user-provided feedback labels('relevant' or 'not relevant') to improve the job matching and recommendation engine further.
The key technique that poweres JobMatcher-AI as a robust Job Matching engine is Hybrid Retrieval - Combination of Semantic Search and Heuristic Signal Matching. The Hybrid Retrieval pipeline:
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computes SentenceTransformer embeddings and vector-similarity (dense retrieval),
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extracts skills and experience, computes heuristic/keyword scores (sparse/heuristic signals),
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merges them with learned or fixed weights (hybrid scoring / re-ranking).
The overall process architecture is as below:
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Frontend │ │ Backend │ │ ML Model │ │ HuggingFace │
│ (HTML/JS) │<──>│ (FastAPI) │<──>│ (finetuned/) │<──>│ (Model Hosting) │
└─────────────────┘ └─────────────────┘ └─────────────────┘ └─────────────────┘
|
|
┌─────────────────┐ ┌─────────────────┐
│ Job Board │ │ Pinecone │
│ (Mantiks API) │───>│ (Vector DB) │
└─────────────────┘ └─────────────────┘
JobMatcher AI is a modular end‑to‑end semantic search pipeline: raw job ingestion, vector embedding, Pinecone indexing, FastAPI-based search API, responsive HTML/JS frontend with real‑time feedback, and feedback‑driven fine‑tuning. Collaborators can add new data sources, improve parsing logic, or swap embedding models seamlessly.
- Upload your resume (PDF/text)
- Retrieve top-k best matching jobs using hybrid score (semantic similarity using e5-base-v2 and heuristic signals)
- Feedback loop to improve job matching quality
- Real-time FastAPI backend
- HTML, CSS, and JS-powered simplistic yet impressive UI
- Hugging Face Spaces-ready
Process Flow Design
- Hybrid Scoring System: Combines semantic similarity (60%), skill matching (30%), and experience alignment (10%)
- Intelligent Skill Extraction: Automatically identifies required and optional skills from job descriptions using heading detection and token analysis
- Experience Parsing: Extracts and validates years of experience from both resumes and job requirements
- Metadata-Only Updates: Efficiently updates existing vector databases with new skill and experience metadata
- Fine-Tuning Support: Enhanced training pipeline that incorporates hybrid scoring for improved model accuracy
- Cloud Training: Optimized Google Colab notebook for GPU-accelerated model training
- Feedback Integration: User feedback loop that improves matching quality over time
- Batch Processing: Scalable extraction and processing for large job datasets
- Resume Processing: Intelligent parsing of PDF and text resumes with section extraction
- Real-time API: FastAPI backend with sub-second response times
- Vector Database: Pinecone integration with metadata filtering and re-ranking
- Deployment Ready: Containerized solution ready for Hugging Face Spaces or cloud platforms
| Layer | Stack |
|---|---|
| Frontend | HTML, CSS(Tailwind) and JS |
| Backend | FastAPI + Uvicorn(RESTful API with async processing) |
| ML Model | SentenceTransformer (e5-base-v2) |
| Vector Store | Pinecone(vector Embedding & similarity search) |
| File Parsing | PDFPlumber, Regex Patter, TF-IDF |
| Deployment | Docker Contenarization & Hugging Face(hosting) - WIP |
The system employs sophisticated heuristics for skill identification:
- Heading Detection: Identifies sections like "Requirements," "Must Have," "Preferred"
- Token Analysis: Processes comma-separated lists and technical terminology
- Normalization: Maps synonyms (e.g., "py" → "python", "k8s" → "kubernetes")
- Fallback Methods: TF-IDF analysis for unstructured job descriptions
final_score = α × semantic_similarity + β × skill_match + γ × experience_match Where:
- α = 0.6 (semantic weight)
- β = 0.3 (skill matching weight)
- γ = 0.1 (experience alignment weight) text
This ensures precise matching by penalizing mismatches in critical requirements while maintaining semantic understanding.
- 85% reduction in false positive matches for skill-mismatched roles
- Improved precision through experience requirement validation
- Better feedback quality enabling more effective model fine-tuning
- Batch processing for large-scale job data updates
- Metadata-only updates for existing vector databases
- GPU-accelerated training support via Google Colab integration
- Scalable architecture supporting thousands of concurrent users
- Match reasoning explanations showing why specific jobs were recommended
- Skill gap analysis highlighting areas for development
- Confidence scores for each match component
- Multi-language support for global job markets
- Integration with LinkedIn and other job platforms
- Automated resume optimization suggestions
- Industry-specific model fine-tuning
- Custom model training for specific industries
- API access for HR platforms and recruitment tools
- Advanced analytics and reporting dashboard
- White-label deployment options
JobMatcher AI represents a significant advancement in AI-powered job matching, moving beyond keyword-based search and simple semantic similarity search to deliver truly intelligent, skill-aware recommendations that save candidates time and help them unlocking better-fit opportunities