Candidate Review Portal

Candidate Review Portal is a full-stack recruitment management platform designed to streamline candidate evaluation workflows. The system enables administrators and reviewers to collaborate on candidate assessments, manage scores, track hiring status, and generate AI-assisted summaries while maintaining secure role-based access control.

Hiring teams often rely on spreadsheets, emails, and disconnected tools to evaluate candidates, leading to inconsistent reviews, poor visibility, and inefficient collaboration. There was a need for a centralized platform where reviewers could score candidates, track evaluation progress, and provide structured feedback while ensuring sensitive information remained accessible only to authorized users.

• Secure User Registration and Authentication
• Role-Based Access Control (Admin & Reviewer)
• Candidate Management and Review Workflow
• Reviewer Scoring and Feedback System
• Advanced Filtering, Search, and Pagination
• AI-Assisted Candidate Summary Generation
• Soft Delete and Candidate Archiving
• Real-Time Score Update Streaming (SSE)

• Built RESTful APIs using FastAPI with SQLAlchemy ORM and SQLite for efficient candidate data management.
• Implemented JWT-based authentication and role-based authorization to enforce secure access control.
• Designed RBAC-specific response models to prevent unauthorized exposure of sensitive candidate information.
• Developed efficient server-side filtering, search, and pagination using database-level queries instead of in-memory processing.
• Implemented Server-Sent Events (SSE) for real-time score update streaming.
• Containerized the application using Docker and Docker Compose for consistent development and deployment environments.
• Built automated integration tests using Pytest and FastAPI TestClient to ensure API reliability.
• Applied clean architecture principles by separating business logic, API routes, authentication, and data access layers.

• Designed and developed the complete application using FastAPI, React, and SQLite.
• Implemented authentication, authorization, candidate management, and review workflows.
• Built secure RBAC mechanisms to control access to candidate data and internal notes.
• Developed filtering, search, pagination, and candidate scoring functionality.
• Integrated AI-assisted summary generation and real-time event streaming features.
• Containerized the application with Docker and created automated API tests using Pytest.
• Documented architectural decisions, debugging solutions, and testing strategies to ensure maintainability.