Resume-Based Job Recommendation System

The Resume-Based Job Recommendation System is a web application that uses large language models and semantic matching to provide personalized job recommendations. Users upload a resume and can provide natural-language preferences to refine their search. The system extracts relevant information, reduces noise, and uses vector embeddings with cosine similarity to match users with relevant job opportunities.

The application uses Python and Flask for its backend and RESTful API, with the backend hosted on Render. Gemini LLMs handle targeted natural language processing, while vector embeddings and cosine similarity perform job matching. The system combines these techniques to provide efficient and consistent recommendations while limiting unnecessary LLM usage.

I developed the Flask backend, RESTful API routes, and Render deployment, while organizing the backend into modular routes and services. I also integrated Gemini access and developed the natural-language preference textbox used to refine job searches. Throughout the project, I assisted with repository management, debugging, and merge conflict resolution.

• See Project Frontend on Github
• See Project Backend on Github