Recruiters in today’s job market are frequently presented with the daunting challenge of screening thousands of resumes for one vacancy position, leading to inefficiency, bias, and lost opportunities. Conventional resume screening processes are not scalable and tend to overlook the best candidates, particularly when subtle skill sets and varying levels of experience are factors. To solve these issues, this paper suggests an automated resume screening system powered by AI that uses Natural Language Processing (NLP) and Machine Learning (ML) to simplify and improve the hiring process. The system is meant to scan unstructured resumes, extract key information like education, skills, experience, and certifications, and compare these features against pre-defined job descriptions. Employing sophisticated NLP methods, the system converts text data into semantic vectors and matches them with job requirement profiles based on similarity measures and classification techniques. It ranks candidates according to their relevance and categorizes them into groups like “fit,” “close fit,” or “not fit,” thereby helping recruiters make informed hiring decisions. In addition, the system features a web-based dashboard through which HR staff and recruiters can upload resumes, enter job specifications, and view candidate matching results in real-time. This easy-to-use interface not only minimizes the cognitive burden on recruiters but also shortens the recruitment cycle and enhances overall recruitment effectiveness. The suggested framework has been tested with real world resume datasets and exhibits high accuracy in matching and classification tasks. With potential uses across industries, this system is a major advancement toward intelligent, equitable, and scalable hiring solutions.
Khatri et al. (Fri,) studied this question.