AI-powered Workforce Intelligence System connecting talent, employers, and education pathways.
This project is a full-stack workforce intelligence platform designed to bridge the gap between job seekers, employers, and training programs. Unlike traditional job boards, this system uses data-driven insights and AI-assisted matching to help users identify skill gaps, career paths, and job opportunities.
The platform combines backend systems, APIs, and data pipelines to simulate a real-world workforce analytics engine.
- Matches users to jobs based on skills, experience, and role requirements
- Uses weighted scoring logic for ranking candidates and job fits
- Tracks required vs. possessed skills per role
- Identifies skill gaps for users and workforce trends
- Job ingestion and normalization layer
- Standardized job model for consistent matching
- Maps training programs to in-demand job skills
- Connects education pathways to real job market needs
- External job data ingestion (e.g., job boards/APIs)
- Structured transformation into internal schema
- Java
- Spring Boot
- Spring Data JPA
- RESTful APIs
- PostgreSQL (production)
- H2 (development/testing)
- Layered architecture (Controller → Service → Repository)
- Domain-driven design principles
- Maven
- Git / GitHub
- Postman (API testing)
- Controller Layer → Handles HTTP requests
- Service Layer → Business logic (matching, scoring, transformations)
- Repository Layer → Database access via JPA
- External API Layer → Job ingestion & normalization
- Computes match percentage between job requirements and candidate skills
- Uses weighted scoring based on skill importance
- Ranks job roles based on demand signals and skill scarcity
- Maps training programs to job skill requirements for career path recommendations
- Users
- Skills
- Jobs
- Training Programs
- Roles
- Skill Mappings (many-to-many relationships)
- Real-world workforce intelligence simulation
- Scalable backend architecture using Spring Boot
- Data normalization pipeline for inconsistent job data
- Practical implementation of matching algorithms
- Strong foundation for AI/ML enhancements in future iterations
GET /api/matches/user/{userId}
GET /api/roles/{roleId}/insights
POST /api/training-programs
- Integrate AI-based recommendation engine (LLM-assisted matching)
- Add React frontend dashboard
- Expand job data ingestion sources
- Add authentication & user profiles (JWT)
- Deploy full system to cloud (Railway / Render)
This project demonstrates full-stack backend engineering ability, including system design, API development, database modeling, and real-world data processing pipelines. It is designed to reflect production-level architecture and scalability considerations.