CareerSage: A Multi-Phase Transformer-Based Framework for Semantic Career Reasoning and Skill Gap Analytics

Career choice forms one of the key steps in shaping a professional journey, which in turn can determine long-term success. In this fast-changing job environment, many people are caught off guard by not being able to make the right career choices that best fit their skills, interests, and goals. This paper introduces CareerSage: A Multi-Phase Transformer-Based Framework for Semantic Career Reasoning and Skill Gap Analytics that makes this process simpler through smart, flexible recommendations. CareerSage requires detailed information like educational background, technical skills, experiences, and objectives to make the guidance more relevant than is possible through typical aptitude tests or job portals. Other platforms, like LinkedIn Learning, Coursera, or AICTE Internship portals, are either learning resources or job listing platforms. At the same time, CareerSage integrates skill development, education, and career advancement as one solution. This system utilizes Generative AI combined with semantic reasoning to gain a better understanding of the users and their skill pathways. It identifies gaps in skills using hybrid machine learning models combined with semantic analysis and cosine similarity, suggesting customized learning opportunities originating from trusted sources like SWAYAM and NPTEL. The AI-driven workflow with industry trends evolves; thus, CareerSage can help users make intelligent and future-ready choices for their careers.