The AI Applicant Tracking System (AI-ATS) is a production-oriented backend project designed to simulate the software used by Human Resources (HR) departments and recruiters to manage the hiring process.
The project emphasizes backend engineering, database design, distributed systems, cloud-native development, and AI integration rather than frontend development. It serves as a comprehensive learning platform covering nearly every important backend concept used in modern software engineering.
The system allows recruiters to create job postings, manage candidates, schedule interviews, evaluate applicants, upload resumes, and leverage AI to analyze candidate-job compatibility.
The project aims to help developers learn and practice:
- REST API development
- Authentication & Authorization
- Relational database design
- SQL optimization
- API security
- Backend architecture
- Distributed systems
- Background job processing
- Caching
- Object storage
- AI integration
- Docker deployment
- CI/CD
- Monitoring
- Production-ready backend practices
- Register account
- Upload resumes
- Apply for jobs
- Track applications
- Receive interview invitations
- View application history
- Create job postings
- Review candidates
- Schedule interviews
- Leave interview feedback
- Manage hiring pipeline
- Review shortlisted candidates
- Compare interview scores
- Make hiring decisions
- Generate offers
- Manage users
- Manage permissions
- Configure system settings
- View analytics
- Audit user activities
Client
│
REST API / JWT
│
┌──────────────┴──────────────┐
│ │
Authentication Business Logic
│ │
└──────────────┬──────────────┘
│
PostgreSQL
│
┌──────────────┼──────────────┐
│ │ │
Redis Object Background
Cache Storage Queue
│ │
Resume PDFs Email / AI
│
LLM / Embedding API
- Register
- Login
- JWT Authentication
- Refresh Tokens
- Password Reset
- Email Verification
- OAuth (optional)
- Candidate Profile
- Recruiter Profile
- Hiring Manager Profile
- Admin Panel
- Role-Based Access Control (RBAC)
- Create companies
- Departments
- Offices
- Recruiters
- Hiring managers
- Create jobs
- Edit jobs
- Archive jobs
- Required skills
- Salary range
- Employment type
- Locations
- Candidate profiles
- Resume versions
- Portfolio links
- Skills
- Education
- Experience
- Certifications
- Upload PDF
- Resume versioning
- Resume parsing
- Extract skills
- Extract education
- Extract experience
Pipeline example:
Saved
↓
Applied
↓
Screening
↓
Interview
↓
Technical Interview
↓
Offer
↓
Accepted / Rejected
- Schedule interviews
- Assign interviewers
- Interview feedback
- Rating system
- Recommendation
- Interview reminders
- Offer notifications
- Application updates
Search candidates by:
- Name
- Skills
- Experience
- Education
- Company
- Job
- Status
Examples:
- Applications per month
- Hiring funnel
- Offer rate
- Time-to-hire
- Average interview score
- Recruiter productivity
Extract:
- Skills
- Experience
- Education
- Projects
- Certifications
Compare:
Candidate Resume
↓
Job Description
↓
Similarity Score
↓
Matching Explanation
Example output:
Required Skills
- Go
- Docker
- Kubernetes
- PostgreSQL
Candidate Skills
- Go
- Docker
- PostgreSQL
Missing
- Kubernetes
Generate recommendations:
- Missing keywords
- Weak experience descriptions
- Resume improvements
Generate:
- Technical questions
- Behavioral questions
- Follow-up questions
- Evaluation rubric
Core tables
- users
- roles
- companies
- departments
- jobs
- candidates
- resumes
- resume_versions
- applications
- interviews
- interview_feedback
- offers
- skills
- candidate_skills
- job_skills
- notifications
- audit_logs
Language
- Go (Gin/Fiber) or FastAPI
Database
- PostgreSQL
Cache
- Redis
Object Storage
- MinIO (S3 Compatible)
Queue
- RabbitMQ
- Redis Queue
- Asynq (Go)
- Celery (Python)
Authentication
- JWT
- Refresh Token
Documentation
- OpenAPI / Swagger
Deployment
- Docker
- Docker Compose
Future
- Kubernetes
- REST
- Versioning
- Pagination
- Filtering
- Sorting
- Validation
- Rate Limiting
- JWT
- RBAC
- Password hashing
- CSRF
- CORS
- Input validation
- SQL Injection prevention
- ER Modeling
- Normalization
- Transactions
- Foreign Keys
- Composite Indexes
- Query Optimization
- Migrations
- Redis caching
- Connection pooling
- Lazy loading
- Batch processing
- Logging
- Error handling
- Retry mechanism
- Background workers
- Dead letter queues
- Unit tests
- Integration tests
- API tests
- Load testing
- Docker
- Docker Compose
- CI/CD
- Health checks
- Monitoring
- Metrics
- Multi-tenancy
- Event-driven architecture
- Microservices
- GraphQL API
- WebSocket notifications
- AI-powered recruiter assistant
- AI chatbot for candidates
- Resume semantic search
- Candidate recommendation engine
After completing this project, you should be comfortable with:
- Designing scalable relational databases
- Building production-ready REST APIs
- Implementing authentication and authorization
- Managing complex business logic
- Working with asynchronous background jobs
- Optimizing database performance
- Using Redis effectively
- Integrating AI services into backend applications
- Deploying backend services with Docker
- Writing maintainable, testable, and scalable backend software
This project is intended to simulate the backend architecture of a modern Applicant Tracking System (ATS), while incorporating AI capabilities commonly found in next-generation HR platforms.