Intelligent Personalized Career Path and Resume-Job Fit System

Abstract

In this project, we present a system that unifies personalized career guidance and resume-Job fit analysis using artificial intelligence. This system gathers user preferences and experience and offers an understandable career roadmap and analyzes the resume of the user with regards to its eligibility according to applicant tracking systems and identifies any skill gaps. Transformer-based resume-Job fit score calculation and suggest edits that can help increase the chances of getting selected for an interview. This project delivers this system in form of an accessible and equitable web app.

Introduction

The text describes the need for an integrated, AI-driven system to support career planning and resume building for students and fresh graduates. Many individuals struggle with selecting suitable career paths, identifying skill gaps, and creating resumes that can pass modern Applicant Tracking Systems (ATS). Existing platforms like LinkedIn, Naukri, and Indeed mainly provide job listings and basic templates but lack personalized guidance, resume improvement insights, and deep job–candidate matching.

To address these limitations, the paper proposes an Intelligent Personalized Career Path and Resume–Job Fit System, a web-based platform that offers end-to-end career support. This system includes features such as user profiling, AI-based resume analysis, skill gap detection, ATS validation, career path recommendations, and job matching. It aims to guide users not only in finding jobs but also in improving their qualifications and resumes to better align with market requirements.

The literature review highlights several existing approaches in career recommendation and resume matching using machine learning and NLP techniques, including transformer-based models and embedding-based similarity systems. While these studies show progress in career prediction and job matching, most are limited in scope. They often focus on either resume scoring or job matching individually, lack explainability, do not provide resume improvement suggestions, and fail to integrate multiple functionalities into a single system. This reveals a clear research gap for a unified platform.

The proposed system addresses these gaps by combining multiple AI components into one framework. It includes resume parsing using NLP, career recommendation engines, job-fit prediction models, and integration with labor market data for real-time insights. The system also emphasizes fairness and explainability in recommendations. Users interact through a web-based interface built using modern web technologies, with backend support for AI processing and data management.

Conclusion

In this paper, the Intelligent Personalized Career Path and Resume-Job Fit System was designed and developed. The use of AI technologies allows integrating the resume parsing, semantic resume-job matching, job listing analysis, and resume improvement in a convenient way. The use of Firebase and web technologies makes the application easily scalable and accessible on both desktop and mobile devices. It allows connecting individual skills of people to job opportunities and helps with better decision-making about career development. The system can improve the employability of people and reduce unemployment in a more informed manner. Finally, the application is consistent with Sustainable Development Goals concerning quality education, decent work and economic growth, innovation, and reduction of inequalities.

References

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Copyright

Copyright © 2026 Vasavi Sravanthi Balusa, Syed Mouzzam Ali, Mohammed Mustafa, Mohammed Abubakr Siddeeq. 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.