JobFit AI

Inspiration

Many job seekers apply for roles without truly knowing whether their resumes match the job requirements. Rejections often come without feedback, leaving candidates confused about what went wrong or how to improve. JobFit AI was inspired by the need to give job seekers clarity, direction, and confidence by using AI to analyze resumes against real job descriptions and provide actionable guidance.

What it does

JobFit AI is an AI-powered resume analysis and job-matching platform. Users provide their resume and a target job description and the system uses AI to: Generate a job-fit score Identify missing or weak skills Highlight experience gaps Provide personalized, role-specific resume improvement suggestions The goal is to help users apply smarter by understanding exactly how well they fit a role and what to improve.

How I built it

JobFit AI was built as a browser-based application using: HTML, CSS, and JavaScript for the frontend PHP for backend logic and API handling MySQL for structured data storage Gemini API for AI-powered resume analysis, semantic matching, and recommendations The AI is called from PHP, where resume and job description inputs are processed and sent to the Gemini API to generate insights, scores, and actionable feedback. The system was designed to ensure AI is central to decision-making rather than a cosmetic feature.

Challenges I ran into

One of the biggest challenges was integrating the AI API reliably. The API integration failed multiple times due to request formatting, response handling, rate limit and prompt structure issues. It took over 21 iterations of testing, debugging, and refining before the AI responses became stable, accurate, and useful.

Another challenge was designing prompts that consistently produce structured, actionable insights rather than generic feedback. I also had to balance response quality with performance to ensure the system remained fast and usable in a real-world setting, while keeping API keys and backend logic secure.

One major challenge was designing AI prompts that produce consistent, useful, and explainable results. Another challenge was balancing depth of analysis with performance, ensuring responses were fast enough for real-time use. Integrating the AI API securely on the backend while keeping the system scalable and safe was also a key challenge.

Accomplishments that I'm proud of

Building a fully functional AI-powered system within the hackathon timeline Deploying the project live so judges and users can test it instantly Ensuring AI directly drives insights, scoring, and recommendations Creating a solution that addresses a real-world problem faced by millions of job seekers

What I learned

I learned how to design AI-driven systems where the model acts as a decision-support engine rather than a chatbot. I also improved my skills in backend API integration, prompt engineering and building end-to-end products that combine frontend, backend, and AI effectively.

What's next for JobFit AI

Future improvements include: ATS optimization and keyword scoring Resume version comparison for different roles User dashboards with analysis history Role-specific resume templates and recommendations Expanded AI explainability for deeper insights