AI-POWERED RESUME SCREENING AND CANDIDATE RANKING SYSTEM

Abstract

Recruitment processes in organizations often involve manual resume screening, which is time-consuming, inefficient, and susceptible to human bias. With the rapid growth of job applications, traditional screening methods fail to scale effectively. This paper presents an AI Powered Resume Screening and Candidate Ranking System developed using full stack web technologies. The proposed system automatically analyzes resumes and matches them with job descriptions using Natural Language Processing (NLP) techniques such as TF-IDF vectorization and cosine similarity. The system ranks candidates based on relevance scores, assisting recruiters in selecting suitable candidates efficiently. The application is developed using Python, Flask, HTML, Tailwind CSS, and a cloud-based database, providing a secure and user-friendly interface. This system reduces manual effort, improves screening accuracy, and enables faster and unbiased recruitment decisions