AI-Driven Resume Analysis and Optimization System for Enhanced Job Market Compatibility

Abstract

Abstract Modern recruitment processes heavily depend on Applicant Tracking Systems (ATS) that automatically filter candidate applications before human review. Job seekers face considerable challenges in crafting resumes that effectively pass through these automated systems while meeting recruiter expectations. This paper presents an innovative artificial intelligencepowered platform that analyzes resume content and provides optimization recommendations to improve job market compatibility. The proposed system integrates multiple Natural language processing constitutes a few among the AI techniques , semantic analysis, and machine learning algorithms to evaluate resume-job description alignment. The platform employs a hybrid architecture combining client-side processing using transformer models with cloud-based enhancement capabilities, ensuring data privacy while delivering comprehensive analysis. Implementation utilizes React framework for user interface development and incorporates PDF processing capabilities for document handling. Experimental evaluation with 100 resume samples demonstrated 89% accuracy correlation with expert assessments and 95% reduction in processing time compared to manual optimization methods. The system achieved 4.2/5 user satisfaction rating and showed significant improvement in ATS compatibility scores across diverse professional domains.