AI-Powered Resume and Career Intelligence System (ARISE)

Author

Yashawanth S A, Tarun B Hegade, Sanju R T , Varun Kumar Nagaraja, Kavyashree P N


Abstract

 Candidates are frequently rejected by Applicant Tracking Systems (ATS) because of poor keyword usage and formatting rather than a lack of skills, and most existing tools offer only job listings or basic resume checks without end-to-end guidance. This paper presents ARISE, an AI-powered full-stack platform that analyzes a candidate's resume, matches it with target job requirements, identifies skill gaps, and converts those gaps into personalized career, learning, and resume-improvement actions. ARISE accepts PDF/DOCX resumes and extracts skills, education, experience, projects, and certifications; computes an explainable 100-point ATS score using eight weighted dimensions; and semantically compares the resume with a job description using Natural Language Processing and Sentence-BERT (all-MiniLM-L6-v2) embeddings with cosine similarity. The detected matched and missing skills drive a four-phase career roadmap and course recommendations, while resume rewriting, skill tracking, resume history, and PDF/DOCX export support continuous improvement. The system is implemented with React, Flask, and SQLite and secured with JWT authentication and bcrypt password hashing. All principal modules are implemented and the major backend service paths are verified through automated functional tests.


Keywords

Resume Parsing, ATS Score, Sentence-BERT, Skill-Gap Analysis, Career Roadmap, Natural Language Processing



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References


Research Papers:

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