AI-Enabled Employment Matching Platform

Abstract

The recruitment process in traditional job portals mainly depends on keyword-based matching, which often leads to irrelevant job recommendations and inefficient candidate selection. Such systems are unable to understand the actual meaning of candidate skills, experience, and job requirements, resulting in poor job–candidate alignment. To overcome these limitations, this paper presents an AI-Enabled Employment Matching Platform that uses Natural Language Processing (NLP) and Machine Learning techniques for intelligent resume-job matching. The proposed system analyzes resumes and job descriptions semantically rather than relying only on keywords. It provides separate dashboards for job seekers and recruiters, enabling resume upload, job posting, candidate shortlisting, and recommendation generation. The platform uses text preprocessing, TF- IDF, cosine similarity, and semantic analysis techniques to compare candidate profiles with job requirements. Recruiters can define skill-based thresholds to shortlist suitable candidates automatically, reducing manual screening effort and saving time. The system improves matching accuracy, reduces hiring costs, and enhances the overall recruitment process. Experimental results show that the proposed model provides better performance than traditional Applicant Tracking Systems (ATS) in terms of precision, recall, and candidate-job relevance. This platform can be useful for recruitment agencies, job portals, educational institutions, and placement cells for efficient and intelligent hiring.