WizJobs — Hybrid AI Job Recommendation Platform
WizJobs Enterprise Production-Scale Hybrid AI Job Recommendation & Talent Intelligence Platform Project Overview WizJobs Enterprise is…
WizJobs Enterprise
Production-Scale Hybrid AI Job Recommendation & Talent Intelligence Platform
Project Overview
WizJobs Enterprise is a production-grade AI-powered job recommendation platform designed to intelligently match candidates with relevant job opportunities using hybrid recommendation algorithms and semantic understanding.
Unlike traditional keyword-based job portals, this system leverages deep learning embeddings, collaborative behavior modeling, and advanced ranking strategies to deliver highly personalized, context-aware, and scalable recommendations.
Problem Statement
- Keyword-based matching produces irrelevant results.
- Traditional systems fail to understand resume semantics.
- Cold-start problem for new users and new job listings.
- No intelligent ranking combining behavior + content.
- Lack of personalization at scale.
Enterprise Solution Architecture
The system is architected as a modular AI platform with separate pipelines for data ingestion, feature engineering, embedding generation, collaborative modeling, and hybrid ranking.
Core Components
- Resume Parsing & Skill Extraction Engine
- SBERT-based Semantic Embedding Pipeline
- Vector Similarity Search Engine
- User-Interaction Matrix for Collaborative Filtering
- Hybrid Weighted Ranking Module
- Analytics & Feedback Loop Optimization
AI & Machine Learning Engine
Content-Based Filtering
- Sentence-BERT Embeddings
- Cosine Similarity Scoring
- TF-IDF Baseline Comparisons
- Semantic Job-Resume Matching
Collaborative Filtering
- User-Job Interaction Matrix
- Implicit Feedback Modeling (clicks, saves, applications)
- Similarity-Based User Clustering
Hybrid Ranking Strategy
Final ranking score is computed using a weighted combination of:
- Semantic Similarity Score
- Collaborative Behavior Score
- Recency Boost
- Popularity Score
- User Preference Weighting
Production-Level Engineering
- Django-based RESTful backend APIs
- PostgreSQL optimized relational database
- Vector indexing for fast similarity search
- Docker containerization
- Nginx reverse proxy configuration
- AWS EC2 cloud deployment
- Secure environment configuration
Real-World Use Case
A Data Analyst uploads a resume containing SQL, Power BI, ETL, and Python skills.
Instead of simple keyword matching, the system:
- Generates semantic embeddings of the resume.
- Compares against vectorized job descriptions.
- Analyzes behavioral data from similar users.
- Applies hybrid ranking logic.
- Returns personalized, context-aware job suggestions.
Scalability & Enterprise Impact
- Handles large job datasets efficiently using vector indexing.
- Supports real-time recommendation generation.
- Cold-start mitigation strategies implemented.
- Designed for integration with enterprise HR systems.
Technology Stack
Python • Django • PostgreSQL • SBERT • Scikit-learn • NumPy • Pandas • Docker • AWS EC2 • Nginx • HTMX • Alpine.js • Tailwind CSS
Project Impact
This project demonstrates the integration of advanced recommendation algorithms with full-stack production engineering, showcasing expertise in AI system design, backend architecture, cloud deployment, and intelligent ranking systems.