Noorbhasha Karishma

https://doi.org/10.64823/ijter.2503027

In today's competitive job market, creating a tailored resume for every job application is both critical and time-consuming. Many organizations use Applicant Tracking Systems (ATS) to filter resumes based on keywords and formatting, often eliminating highly qualified candidates whose resumes are not optimized. This project presents an AI powered Resume Builder that automates the process of customizing resumes based on job descriptions. The system leverages Natural Language Processing (NLP) to extract relevant keywords from job postings and matches them against user provided resumes. Using the Affinda API for resume parsing and a Python Flask backend, the application intelligently updates and formats resumes to improve keyword match rates and ATS compatibility. The resulting resumes are exportable in DOCX and PDF formats, significantly enhancing the candidate's chances of getting shortlisted while reducing manual effort.