Resume Matcher

Our AI agent instantly analyzes your resume against any job description, providing actionable feedback to help you beat the ATS bots, customize applications, and land your dream job faster and easier.

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Inspiration

As a graduate student actively looking for opportunities to gain real-world experience, it is increasingly difficult to stand out among the sea of applicants.

What it does

Our application provides a simple and powerful way for job seekers to gain a competitive edge. The process is straightforward:

  1. Upload Your Resume: Users can upload their resume as a .pdf or .txt file.
  2. Paste the Job Description: Users paste the full text of the job description they're targeting.
  3. Get Instant AI Analysis: With a single click, our agent performs a deep analysis and generates a comprehensive report right in the browser.

The output is a detailed Match Report designed to be immediately useful, providing:

  • Match Score: A percentage score indicating how well the resume aligns with the job's requirements.
  • Expert Analysis: A concise summary from the AI career coach highlighting overall strengths and weaknesses.
  • Matched Keywords: A list of skills and experiences that align perfectly with the job description.
  • Missing Keywords: A list of crucial qualifications from the job description that are missing from the resume.
  • Actionable Suggestions: Specific, actionable advice on how to edit the resume to improve the match score.

How we built it

We built this agent by combining a powerful large language model with a fast, interactive web framework.

  • AI Engine & Backend: The core logic is powered by Google's Gemini 2.5 Flash model. We used the LangChain framework to orchestrate the AI's workflow. The "brain" of our agent is a meticulously crafted Prompt Template that instructs the AI to act as an expert career coach and structure its analysis in a specific format.

  • Frontend User Interface: The entire web application is built with Streamlit. This allowed us to rapidly develop a clean, responsive, and user-friendly interface for file uploads and text input.

  • Document Processing: To handle resume files efficiently, we integrated the PyMuPDF (fitz) library, which offers high-performance text extraction from PDF documents.

  • Key Libraries & Technologies:

    • AI/ML: langchain, langchain-google-genai
    • Frontend: streamlit
    • Document Parsing: PyMuPDF
    • Language: Python

Challenges we ran into

This is my first endeavour into the AI Agent space, and I was required to do a considerable amount of research before getting started on the project. I was required to learn an entirely new library in a few days and build the entire system.

Accomplishments that we're proud of

Giving users the ability to use the AI Agent by hosting it locally through Streamlit.

What we learned

  • The LangChain library, which allows you to create AI Agents.
  • The Streamlit library, which allows you to create a robust frontend UI to showcase your AI/ML solutions.
  • Spending more time researching relevant libraries to improve the efficiency of the AI Agent.

What's next for Resume Matcher

  • Improve the model analysis of a resume by incorporating other LLMs to aggregate responses.
  • Expand support for other formats of resume documents, like docx and LaTeX.
  • Improve base prompt structure to enhance the analysis of the resume and provide better Actionable Suggestions.

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