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

Abstract

An artificial intelligence-powered Applicant Track-
ing 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 determin-
istic experience-scoring model power the system’s hybrid scor-
ing 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.