OffHeadHunter is an AI-powered job search assistant designed to personalize and streamline the recruitment journey. Through a conversational interface, it captures user preferences, analyzes CVs, and matches candidates with relevant job offers—culminating in a Kanban-style dashboard that tracks the application process end-to-end.
This project is being developed collaboratively over a 3-week sprint cycle as part of a bootcamp final challenge.
- Chatbot-based interaction for job search input
- CV upload and vectorization
- Match Score between CV and job offers
- Scraped job listings from selected portals (LinkedIn, InfoJobs, Indeed)
- Adapted CV generation with LLMs
- Application tracking via visual dashboard (Kanban flow)
- User registers and uploads CV
- Bot gathers search preferences
- System scrapes portals + vectorizes CV
- Top offers are selected based on match score
- Dashboard organizes applications by status
| Area | Tools / Frameworks |
|---|---|
| Backend / Logic | Python, FastAPI, N8N, Scrapy |
| Database | MongoDB (planned), Qdrant for vector search |
| Frontend / UI | Streamlit, HTML/CSS/JS, Bolt.io, Lovable |
| AI Components | Gemini API, HuggingFace Models, RAG + LLMs |
| TTS/STT (Optional) | Whisper + Coqui / Google TTS (if time permits) |
- Python 3.10+
- MongoDB local or Atlas
- Virtual environment (recommended)
- Git + GitHub account
git clone https://github.com/Rarvelom/OffHeadHunter-Reboot-Final-Project.git
cd OffHeadHunter-Reboot-Final-Project
python -m venv env
source env/bin/activate # or env\Scripts\activate on Windows
pip install -r requirements.txt