AI-Powered Recruitment and Interview Platform: An Intelligent System for Resume Analysis, Candidate Screening, and Interview Preparation

Abstract - Manual resume screening remains the default approach in recruitment pipelines but it does not hold up well against the volume of applications generated by modern job portals and it is susceptible to inconsistency and bias. The AI-based tools that have emerged to address this problem tend to handle one stage of the pipeline at a time. Resume parsing, keyword matching or a fixed bank of interview questions. With little integration across resume analysis, semantic candidate-job matching and interview preparation. This paper reviews work in Natural Language Processing (NLP) transformer-based semantic matching and Large Language Model (LLM)-based recruitment and interview systems and on that basis proposes a conceptual AI-Powered Recruitment and Interview Platform that brings together resume parsing, candidate-job matching, explainable screening and resume-aware interview preparation with personalized feedback in one framework. Fairness, privacy, explainability and human oversight are treated as core requirements of the design than features added afterward. The framework has not been implemented or experimentally validated; it is offered as an architecture, built from the surveyed literature intended to guide future work on fair and effective AI-assisted recruitment systems.