Smart Job Description and Curriculum Vitea Using AI

Abstract

The recruitment process has also become more challenging due to the number of resumes, making it difficult for recruiters to find the right candidate for the position based on the requirements of the job posting. Therefore, a manual recruitment process has proven to be ineffective, leading to the development of the Smart Job Description and Curriculum Vitae Matching System using Artificial Intelligence for the recruitment process. In the proposed project, the resumes and job descriptions will be compared using Natural Language Processing techniques for the extraction of skills, qualifications, and experience of the candidate for the position. The proposed project will be useful for recruiters by comparing the candidate’s profile with the job requirements and generating a score for the matching of the candidates for the position, making the recruitment process more efficient and effective for the recruiters. In addition, the proposed project will also be useful for the candidates by providing information about the skills that need to be learned for the betterment of the resume, making the project more useful for the candidates by providing information about the resume and the required skills for the resume.

Introduction

The text discusses the challenges of modern recruitment, where large volumes of applications make candidate screening inefficient and time-consuming, while candidates struggle to tailor resumes effectively. Traditional methods and basic tools lack accuracy, context understanding, and scalability, leading to slow and biased hiring processes.

To address these issues, the Smart Job Description and Curriculum Vitae Using AI system is proposed. It leverages AI and NLP to automate resume screening and job matching by analyzing job descriptions and resumes, extracting key skills, and computing compatibility scores. The system also provides feedback to candidates to improve their resumes and assists recruiters in making faster, data-driven decisions.

The system architecture includes modules for resume/job processing, an AI matching engine using semantic analysis, ATS scoring with career guidance, analytics, and user management. It uses modern technologies such as React.js, FastAPI, Python NLP libraries, and databases like PostgreSQL and MongoDB.

Advanced NLP techniques—such as Named Entity Recognition, semantic embeddings, and skill normalization—enable accurate extraction and matching of skills, experience, and qualifications. A multi-step matching algorithm ranks candidates, identifies skill gaps, and generates recommendations.

Key design principles include fairness, transparency, scalability, data security, and user-centric design. The system also incorporates bias reduction, explainable results, and compliance measures.

Overall, the proposed system enhances recruitment efficiency, improves matching accuracy, reduces manual effort, and provides intelligent career guidance, contributing to a faster, smarter, and more reliable hiring process.

Conclusion

This research proposes an AI-driven Job Description and CV Matching System with the aim of improving the recruit- ment process and career guidance. The system is developed with the aim of improving the accuracy of the hiring process while at the same time assisting candidates with the improve- ment of their skills. The architecture is developed with the aim of promoting transparency, protecting the privacy of the user, and establishing trust with the user through the use of role-based access control and explainable scoring systems. Experimental results show the improved relevance of the matching system, robust privacy guarantees, and the perfor- mance of the system. The proposed system is a powerful and intelligent solution for the recruitment challenges experienced today. As AI continues to change the recruitment landscape, the proposed system provides a foundation for transparent recruitment support tools.

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Copyright

Copyright © 2026 Sravanthi B, Narendra Potla, Khadri Shaik, Venubabu Valaparla, Jagadeesh Thallapaneni, Raju Mithra Kantheti. 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.