AI Resume Ranker

Abstract

The increasing volume of job applications in modern recruitment pipelines has created a pressing need for automated, accurate, and unbiased candidate screening. The AI Resume Ranker addresses this challenge by leveraging advanced Natural Language Processing (NLP), machine learning, and semantic similarity techniques to evaluate and prioritize resumes based on job-specific requirements. The system extracts key features such as skills, experience, education, achievements, and domain-relevant keywords, transforming unstructured resume text into structured, comparable representations. Using models such as BERT, similarity scoring algorithms, and supervised ranking methods, the Ranker generates a relevance score for each candidate and orders resumes according to their predicted fit for the role. Additionally, the solution integrates mechanisms to reduce bias by anonymizing certain attributes and applying fairness-aware evaluation metrics. A user-friendly interface enables recruiters to upload resumes, view ranked outputs, analyse candidate strengths, and customize scoring parameters according to job descriptions. By automating the initial screening process, the AI Resume Ranker significantly reduces hiring time, enhances decision consistency, and improves the quality of shortlisted candidates. This system demonstrates how AI can optimize recruitment workflows by combining linguistic intelligence, data-driven insights, and ethical design principles