International Journal of Scientific Research and Engineering Development

In today’s competitive job market, recruitment demands efficient, unbiased, and intelligent tools to evaluate candidates. This project integrates Machine Learning (ML) and Natural Language Processing (NLP) to develop an AI-powered recruitment assistant that automates and enhances multiple stages of the hiring process. The system extracts key information such as skills, education, and experience from resumes, and matches them with job descriptions using semantic similarity models. Additionally, it generates personalized interview questions based on the candidate’s profile and creates customized cover letters tailored to specific job roles. By uniting resume screening, interview preparation, and document generation, the system optimizes recruiter efficiency, minimizes human bias, and improves the candidate experience. The results demonstrate that AI-based automation significantly reduces screening time and ensures fair, data-driven hiring decisions.