Smart ATS: An AI-Driven Multi-Stage Resume Scoring and Recruitment Automation System

Abstract

An artificial intelligence-powered Applicant Tracking System (ATS) that uses a multi-step algorithmic pipeline to handle candidate scoring, skill finding, experience analysis, and resume extraction. The Sentence-BERT model (allMiniLM-L6-v2) for job-description similarity, RapidFuzz for fuzzy skill matching, canonical skill-mapping algorithms, and a deterministic experience-scoring model power the system's hybrid scoring architecture.
Using weighted evaluation characteristics such as skill relevance, experience alignment, LLM-based semantic matching, and penalty adjustments for underqualification or overqualification, the proposed ATS calculates a normalised 0–10 score. Experimental review on a dataset of over 40 resumes demonstrates a screening accuracy improvement of over 88\% when compared to manual evaluation methodologies, significantly reducing HR workload and producing consistent and intelligible applicant rankings.