An AI-Driven Resume Screening and Candidate Ranking System Using NLP and Semantic Analysis

Abstract

Modern recruitment systems face challenges because organizations receive excessive job applications which require manual resume checks that take too much time and lead to unfair hiring outcomes. The latest development in artificial intelligence (AI) and natural language processing (NLP) technology now supports organizations to automate their data-intensive operations. The research presents an AI-driven Applicant Tracking System (ATS) which automatically performs resume evaluations and candidate selection processes. The proposed system extracts structured information from resumes using NLP techniques and evaluates candidate profiles by matching skills, qualifications, and experience against job requirements. The system uses a web-based structure that enables users to interact through a React.js front end while AI technology processes resumes and assesses candidates in the back end. The system allows recruiters to evaluate resumes through multiple formats which include PDF and DOCX while generating relevance scores that simplify decision-making. The experimental results show that the designed ATS system outperforms traditional recruitment methods through better screening efficiency and consistent results and enhanced system capacity. The study demonstrates that AI-driven automation systems can improve both recruitment efficiency and fairness by providing a solution which enterprises can use to manage their hiring procedures.