AI-Powered Resume Ranking System: Enhancing Recruitment Efficiency through Natural Language

Recruiters routinely face the challenge of evaluating large volumes of applicant resumes within limited timeframes, often leading to delays, subjective decisions, and inconsistencies in candidate shortlisting. This study proposes an AI-powered resume ranking system that leverages Natural Language Processing (NLP) and machine learning to automate and enhance the early stages of recruitment. The system extracts key candidate information using advanced text-processing techniques, transforms unstructured resume data into structured representations, and applies semantic similarity matching to align applicant profiles with job requirements. Multiple ranking models—including TF-IDF, word embeddings, and transformer-based encoders—are evaluated to determine the most effective approach for accuracy, fairness, and scalability. The research further incorporates bias-mitigation strategies and explainability features to support transparent decision-making. Experimental results demonstrate that the proposed system significantly improves screening speed while maintaining or enhancing the quality of candidate selection compared with traditional manual methods. Overall, this work highlights the potential of AI-driven NLP solutions to streamline recruitment workflows, reduce human bias, and support data-driven talent acquisition.