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Machine Learning Powered Resume Optimization for ATS

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Machine Learning Powered Resume Optimization for ATS


Mrunali Bandu Vaidya



Mrunali Bandu Vaidya "Machine Learning Powered Resume Optimization for ATS" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Advancements and Emerging Trends in Computer Applications - Innovations, Challenges, and Future Prospects, March 2025, pp.837-842, URL: https://www.ijtsrd.com/papers/ijtsrd79675.pdf

In today’s competitive job market, optimizing resumes for Applicant Tracking Systems (ATS) is crucial for job seekers. Many applicants struggle with keyword optimization, formatting, and alignment with job descriptions, leading to lower chances of getting shortlisted. Machine Learning-Powered Resume Optimization for ATS is an AI-driven software designed to create professional, ATS-compliant resumes with minimal effort. The system is built using React.js and Next.js for an intuitive frontend and Java/Spring Boot for efficient backend processing. It integrates the Llama 3 model hosted on Groq’s AI platform, which analyzes user inputs and generates tailored resumes based on job-specific requirements. Through a structured form, users input their information, and the AI optimizes the material by making sure it is formatted correctly, has keyword enrichment, and is compatible with applicant tracking systems. Job-ready resumes in a variety of formats are generated by the program, which automates the resume-building process. This approach uses machine learning to increase relevance, visibility, and ranking in ATS screenings, in contrast to conventional resume generators. It increases the success rates of job applications while decreasing manual labor by simplifying resume optimization. By making sure resumes get beyond applicant tracking systems (ATS) and effectively reach hiring managers, this project seeks to close the gap between recruiters and job searchers.

Resume Optimization, Applicant Tracking System(ATS), Natural Language Processing(NLP), Keyword Optimization, Spring Boot, React.js, Next.js, Groq AI, Llama 3 Model, Resume Parsing, ATS Compliance, Job Matching


IJTSRD79675
Special Issue | Advancements and Emerging Trends in Computer Applications - Innovations, Challenges, and Future Prospects, March 2025
837-842
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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