This research paper presents a Smart Resume Analyzer, an AI-assisted system designed for automated resume evaluation and job matching. The system combines traditional Applicant Tracking System (ATS) scoring with modern Artificial Intelligence techniques such as Large Language Models (LLMs) and transformer-based sentence embeddings to improve recruitment efficiency. It analyzes resumes by calculating ATS scores, detecting important sections, extracting relevant skills, predicting suitable job roles, generating resume summaries, and providing personalized improvement suggestions. Additionally, the system includes a Job Description (JD) matching module that compares multiple resumes with a given job description and ranks them using semantic similarity, skill matching, and ATS scoring. The system uses Flask as the backend, LLaMA 3 for job role prediction and summarization, and all-MiniLM-L6-v2 for semantic similarity-based ranking. Experimental evaluation shows strong performance in skill extraction, resume ranking, and recruiter support, making the system useful for both job seekers and hiring teams.
Shekhare et al. (Sun,) studied this question.