Objective: Optimise the recruitment process by automating candidate management and evaluation through AI, centralising applications and reducing manual workload.
Main Objectives & Features
Centralise applications into a single platform to avoid scattered CVs and manual tracking.
Intelligent CV matching to automatically rank and surface the best candidates for open positions.
Automated pre-screening interviews (voice/chat or questionnaire) to filter candidates before human interviews.
Reduce human bias by applying consistent evaluation criteria and AI-assisted scoring.
Provide recruiters with a smart dashboard for decision support and analytics (candidate pipeline, metrics, and recommendations).
Technical Stack & Tools
Backend: Spring Boot, Spring Security, Keycloak for authentication and authorization.
Databases: PostgreSQL and/or MongoDB for relational and document storage needs.
Frontend: React for building the recruiter and candidate interfaces.
AI: Integration of AI tools for CV parsing, semantic matching, automated interview/questionnaire processing, and bias mitigation.
Expected Deliverables
A working prototype of an Intelligent and Automated Recruitment Platform with core flows: application intake, CV matching, pre-screening automation, and recruiter dashboard.
Documentation covering architecture, data model, security (Keycloak integration), and deployment instructions.
Tests and demo scenarios illustrating improvements to time-to-hire and candidate ranking quality.
Skills expected: Java (Spring Boot), familiarity with Spring Security and Keycloak, frontend experience with React, basic knowledge of PostgreSQL/MongoDB, and interest/experience with AI/ML tools for NLP and matching.
Soft skills: ability to work in a team, good communication, problem-solving and interest in HR/recruitment domain automation.