AI Job Finder Pro

An AI-powered platform that helps users discover, match, and apply for jobs efficiently.

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AI Job Finder Pro

Inspiration

As a student, I realized that searching for internships and jobs takes a lot of time. Most job seekers visit multiple websites, upload the same resume repeatedly, and manually apply for every opportunity. I wanted to build a single platform that makes this process simpler, faster, and more organized.

What it does

AI Job Finder Pro is a Python-based web application that helps users search for jobs, upload and parse resumes, and discover relevant opportunities from multiple job platforms.

The application allows users to:

  • Upload and parse PDF resumes
  • Search jobs from multiple job portals
  • Match resumes with suitable job opportunities
  • Automate the job application process
  • Track application progress and statistics
  • Choose between headless and visible browser automation

How I built it

I built AI Job Finder Pro using Python and Streamlit for the user interface. The project uses Selenium for browser automation, BeautifulSoup for web scraping, PDFMiner for resume parsing, and Requests for handling web requests.

During development, I used Antigravity AI as an AI-assisted coding tool to help with code generation, debugging, and implementation. I reviewed, tested, integrated, and refined the generated code, fixed issues, and ensured the application worked correctly before deployment.

The project is organized into separate modules for resume parsing, job scraping, job matching, and automated job applications, making it easier to maintain and improve.

To make the project more modular, I separated the application into different components for:

  • Resume Parsing
  • Job Scraping
  • Job Matching
  • Automated Job Applications

The application is deployed using Streamlit Community Cloud, with the source code hosted on GitHub.

Challenges I faced

One of the biggest challenges was making browser automation reliable across different environments. During development I encountered:

  • ChromeDriver compatibility issues on Windows
  • Unicode encoding errors in the terminal
  • Job scraping failures due to website structure changes
  • Integrating the automation workflow into the Streamlit interface
  • Improving the stability and overall user experience

Debugging and solving these issues helped make the project much more reliable.

Accomplishments I'm proud of

I'm proud that I was able to build a complete job search platform that combines resume parsing, job matching, and browser automation into one application.

I'm also happy with the improvements made during development, including:

  • Better job scraping reliability
  • Improved Windows compatibility
  • Integrated auto-application workflow
  • Cleaner and more user-friendly interface
  • Live application progress and statistics

What I learned

This project gave me hands-on experience with:

  • Python application development
  • Streamlit
  • Selenium automation
  • Web scraping
  • Debugging real-world issues
  • Building modular applications
  • Deploying applications to the cloud

It also taught me the importance of testing, problem-solving, and continuously improving software based on real-world challenges.

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