JobFit-Pro

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A windows application for tailoring resumes to job applications.

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Primary Language

Python

Last Updated

January 17, 2026

Tech Stack

PythonOpenAIPyQt6

About This Project

JobFit-Pro is a Windows desktop application designed to help job seekers evaluate and improve how well their resumes align with specific job descriptions. The application provides structured analysis of skills, keywords, and relevance, transforming unstructured resume text into clear, actionable feedback. Built entirely as a native desktop application using Python and PyQt, JobFit-Pro prioritizes local performance, privacy, and focused workflows. Resumes and job descriptions are processed locally on the user’s machine, ensuring sensitive career data is not stored or transmitted unnecessarily. This approach allows for fast analysis while maintaining strong user trust. The application integrates OpenAI’s API to generate intelligent, context-aware feedback that follows modern resume best practices. Rather than relying on generic AI output, JobFit-Pro uses carefully designed prompts to guide the model toward concise, practical recommendations such as skill gaps, keyword improvements, and resume optimization strategies. The architecture separates UI, processing logic, and AI integration to support maintainability and future expansion.

My Role & Contributions

I designed and developed JobFit-Pro end to end as a standalone Windows desktop application. This included building the PyQt-based user interface, structuring the application architecture, and implementing the core text analysis and AI integration logic. I was responsible for parsing resume and job description text, implementing keyword extraction and similarity scoring, and integrating OpenAI’s API to generate resume improvement recommendations. I also designed and refined prompt structures to ensure AI output aligned with current resume best practices. Throughout development, I focused on clean separation of concerns, responsive UI behavior, and maintainable Python code.

Key Features

✓Native Windows desktop application built with Python and PyQt
✓Local resume and job description text processing for privacy and speed
✓Keyword extraction and skill overlap analysis using Python NLP techniques
✓AI-powered resume feedback generated through OpenAI API integration
✓Prompt-engineered AI responses aligned with modern resume best practices
✓Structured, user-friendly interface for reviewing analysis results
✓Modular architecture enabling future scoring models and feature expansion

Challenges & Solutions

One challenge was handling the wide variety of resume formats and writing styles while maintaining consistent analysis results. This was addressed by implementing flexible text preprocessing and normalization logic in Python to ensure reliable keyword extraction across different document structures. Another significant challenge involved designing effective prompts for the OpenAI integration so that AI-generated feedback aligned with modern resume best practices rather than producing generic or unfocused suggestions. I solved this by iteratively refining prompt structure, explicitly defining the AI’s role, constraining output formats, and grounding responses in actionable resume guidance. This resulted in clearer, more consistent, and more practical recommendations that users could immediately apply.

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Source Code

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