AI-Powered Resume Screening and Job Recommendation System

Recruitment teams may receive a large volume of resumes for a vacancy, making manual screening time-consuming andinconsistent. Job seekers may also struggle to identify vacancies that align with their skills, education, experience, andinterests. This paper proposes an AI-powered system that extracts structured information from resumes, compares candidateprofiles with job descriptions, and recommends relevant opportunities. The pipeline combines document parsing, textpreprocessing, skill extraction, TF-IDF or sentence-embedding representations, and a transparent matching score.Candidate-facing results explain why a job was recommended, while recruiter-facing results support—rather than replace—human review. The design includes privacy controls, bias evaluation, and mechanisms for correcting extracted information.A reproducible evaluation plan uses extraction quality, Precision@K, Recall@K, ranking metrics, and subgroup erroranalysis. No experimental results are claimed because the proposed system has not been benchmarked for this manuscript.