In the context of a rapidly evolving digital economy, the recruitment process is increasingly relying on automated systems and data-driven decision-making. The Curriculum Vitae (CV) remains a central element in evaluating candidates; yet, many job seekers, especially students and recent graduates, face challenges in aligning their profiles with the expectations of employers. Existing CV creation tools mainly emphasize design aesthetics and template formatting, while lacking semantic analysis, personalized feedback, and skill development pathways. This study introduces a smart CV analysis and recommendation system that integrates techniques from Natural Language Processing (NLP), Optical Character Recognition (OCR), and advanced large-scale language models (LLMs). The system is capable of parsing and analyzing candidate CVs, measuring their relevance against job descriptions (JDs), identifying strengths and deficiencies, and generating customized skill development roadmaps accompanied by targeted practice exercises. Furthermore, it supports automated CV generation in standardized formats optimized for applicant tracking systems. Experimental evaluation with a cohort of final-year university students demonstrates that the system substantially improves the alignment between CVs and job requirements, reduces preparation time, and enhances candidate confidence. These findings highlight the potential of AI-driven CV analysis systems as a bridge between academic training and labor market demands.