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An open-source Django recruitment management platform that integrates AI resume screening, automatic matching scoring, candidate profile analysis, and other functions, demonstrating how to combine traditional HR systems with machine learning to improve recruitment efficiency.
Section 01
An open-source Django recruitment management platform that integrates AI resume screening, automatic matching scoring, candidate profile analysis, and other functions, demonstrating how to combine traditional HR systems with machine learning to improve recruitment efficiency.
Section 02
Section 03
Amid the wave of digital transformation, human resource management is undergoing profound changes. Traditional recruitment processes rely on manual resume screening and job requirement matching, which are not only time-consuming and labor-intensive but also prone to subjective bias. AI-Recruit-Platform emerged as a solution; it is an open-source intelligent recruitment management system built on the Django framework, aiming to optimize the entire recruitment process from job posting to candidate hiring through artificial intelligence technology, enabling data-driven decision support.
The project adopts a front-end and back-end separation architecture, with the back-end providing API services based on Django REST Framework and the front-end using a modern web technology stack. The core highlight of the system lies in its built-in AI-driven resume screening and candidate matching scoring mechanism, which can automatically evaluate the fit between candidates and positions, significantly reducing the workload of HR personnel.
Section 04
The project's backend uses the Django framework, a mature and stable Python web framework known for its "batteries included" design philosophy. The system is divided into multiple Django Apps by functional modules:
Section 05
The system adopts RESTful API design, implemented via Django REST Framework. The authentication mechanism uses JWT (JSON Web Token), with token issuance and refresh functions provided by the rest_framework_simplejwt library. The API documentation follows the OpenAPI 3.0 specification, integrating drf-spectacular to automatically generate Swagger documents. Developers can view the complete API documentation via the /api/docs/ endpoint.
Main API endpoints include:
/api/auth/login/ - User login/api/auth/logout/ - User logout/api/auth/refresh/ - Token refresh/api/dashboard/stats/ - Dashboard statistics/api/master/ - Master data management/api/ - Core business APIs (jobs, candidates, applications)Section 06
The Job Requisition module supports full lifecycle management of recruitment requirements. HR can create job requisitions and set attributes such as department, job level, employment type, work location, salary range, minimum/maximum experience requirements, etc. Each job can be associated with required skill tags, and the system supports candidate screening based on skill matching degree.
Job requisitions support workflow state management: draft, open, closed. Published jobs are available for candidates to apply for, and the system automatically tracks the number of applications and status distribution.
Section 07
The Candidate module records the complete information profile of job seekers, including basic information (name, email, phone), work experience (current company, position, total work years), educational background, skill tags, etc. The system supports calculating an AI profile score (ai_profile_score) for candidates, which is comprehensively computed based on the number of skills and work experience of the candidate, helping HR quickly identify high-potential candidates.
Candidate data sources are diverse, including manual entry, import from recruitment websites, or internal referrals. Each candidate has a unique candidate ID for easy tracking and management.
Section 08
The Application module connects job requisitions and candidates, recording candidates' application behaviors. Application statuses include: applied, screening, ai_screening, shortlisted, interviewing, hired, rejected, withdrawn.
The system supports two screening modes:
Manual Screening: HR or recruitment managers manually review application materials, recording review comments and rejection reasons.
AI Automatic Screening: The system automatically calculates an AI matching score (0-100 points) based on the skill matching degree and experience requirements of the job. When the score reaches the set threshold, the candidate automatically enters the shortlist. The AI screening process generates detailed screening explanations, including skill matching status, experience comparison, final score, and whether the candidate is automatically shortlisted.
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