JobFit-AI - AI Powered Smart Resume & Job Match Analyzer

Abstract

The recruitment process today relies heavily on Applicant Tracking Systems (ATS), which often act as strict gatekeepers. Studies suggest that nearly 75% of qualified resumes are rejected by these systems, often due to simple formatting errors or missing keywords rather than a lack of skills. To address this issue, this paper proposes \"JobFit-AI\" a web application developed to help candidates navigate automated screening. The system is built using the MERN stack (MongoDB, Express.js, React, Node.js). Unlike traditional parsers that rely on basic keyword counting or Cosine Similarity, our approach integrates the Google Gemini API. This allows for semantic analysis, enabling the system to understand the actual context of a resume PDF when compared to a job description. Leveraging Large Language Models (LLMs), the application generates a \"Fit Score,\" identifies missing skills, and offers specific suggestions for improvement. Testing indicates that this LLMbased method provides significantly higher accuracy than standard string-matching techniques, as it utilizes advanced prompting to interpret a candidate\'s true potential..

Introduction

Recruitment processes have evolved with the use of Applicant Tracking Systems (ATS), which help organizations handle large volumes of applications but often create challenges for job seekers, especially fresh graduates. Existing resume analysis tools mainly rely on general scoring or keyword matching, which lack contextual understanding, personalization, and actionable feedback.

This study introduces JobFit-AI, a system designed to overcome these limitations by focusing on target-specific resume optimization. Unlike traditional tools, it compares a candidate’s resume directly with a job description using advanced AI (Google Gemini), enabling contextual understanding and providing tailored suggestions for improvement.

The system is built using a three-tier MERN architecture (React, Node.js, MongoDB) and integrates generative AI through prompt engineering. It processes resumes by extracting text, combining it with job descriptions, and generating structured outputs such as match scores, missing skills, and improvement tips.

Implementation results show high efficiency, with response times of 2–3 seconds and approximately 90% accuracy compared to human recruiter evaluations. User feedback indicates that the system’s personalized suggestions significantly improve resume quality and alignment with job roles.

Overall, JobFit-AI offers a more intelligent, dynamic, and user-centric approach to resume analysis, bridging the gap between automated screening systems and applicant needs by acting as a personalized career guidance tool.

Conclusion

The development of JobFit-AI demonstrates the practical utility of integrating Generative AI with standard full-stack web architectures. By moving beyond the limitations of static keyword matching the system effectively addresses the \"black box\" problem of modern hiring. The successful implementation of the MERN stack alongside the Gemini API proves that semantic analysis can provide candidates with actionable insights, helping them identify and bridge the gap between their current skills and industry requirements.

Copyright

Copyright © 2026 Vasu ., Sunidhi , Sudhanshu , Prince Gautam, Mrs. Ritu Singh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.