Rankitech – Backend

A scalable, AI-powered backend system for automating job description and consultant profile matching. This system compares skills and experience using intelligent scoring, ranks top candidates, and automates recruiter communication. Built with FastAPI, supports Docker, uses Celery + Redis for async tasks, and follows modular architecture for production-readiness.

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Features

• Intelligent matching of job descriptions to consultant profiles
• Customizable scoring based on skill overlap and experience
• Rank and return top 3 consultant matches
• Email notification simulation (can be integrated with real SMTP)
• Modular and scalable backend using FastAPI
• Task queue support via Celery + Redis (or RabbitMQ)
• API-ready for frontend integration
• Dockerized and GitHub-ready with Python virtual environment support

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Project Structure

rankitech-backend/
│
├── app/
│   ├── __init__.py
│   ├── main.py                # Entry point for FastAPI app
│   ├── config.py              # Settings & environment variables
│   ├── models.py                # Pydantic models
│   ├── api/                   # API routes
│   └── tasks.py                 # Background tasks (Celery)
│
├── tests/                     # Unit and integration tests
│
├── Dockerfile                 # Docker configuration
├── docker-compose.yml         # Dev environment (API + Redis)
├── requirements.txt           # Python dependencies
├── .env.example               # Example env variables
├── README.md                  # Project documentation

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Getting Started

1. Clone the Repo

git clone https://github.com/your-org/rankitech-backend.git
cd rankitech-backend

2. Set Up Environment

Create a virtual environment:

python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

Create a .env file based on .env.example.

3. Run the App

uvicorn app.main:app --reload

4. Run with Docker

docker-compose up --build

This will start:

• FastAPI backend at http://localhost:8000
• Redis (for background tasks)

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Tech Stack

• Language: Python 3.13
• Framework: FastAPI
• Task Queue: Celery
• Message Broker: Redis
• Containerization: Docker & Docker Compose
• Testing: Pytest
• Version Control: Git, GitHub

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Security & Scalability

• Followed 12-factor app methodology
• Supports deployment on cloud-native platforms
• Modular design allows plug-and-play with real databases and SMTP

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Testing

pytest tests/

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Deployment

You can deploy this backend using:

• Docker (recommended)
• AWS/GCP/Azure (container instances)
• Any VM or bare-metal server (via uvicorn)

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License

This project is under the MIT License. See LICENSE for details.

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