AI POWERED RESUME SCREENING AND CANDIDATE RANKING SYSTEM FOR EFFICIENT RECRUITMENT

Abstract

The exponential growth of online job applications has made manual resume screening inefficient and prone to bias. This paper presents an AI-Powered Resume Screening and Candidate Ranking System that automates recruitment using Natural Language Processing and Machine Learning techniques. The system evaluates resumes against job descriptions and computes Applicant Tracking System (ATS) scores based on skill relevance, experience, and textual similarity. It supports PDF and DOCX resumes and provides transparent rejection feedback to candidates. Recruiters can efficiently rank applicants using an intuitive dashboard. Experimental results demonstrate reduced screening time, improved consistency, and enhanced decision-making, making the system suitable for large-scale and data-driven recruitment. KEY WORDS: Resume Screening, Applicant Tracking System, Natural Language Processing, Machine Leaning, Candidate Ranking