Rapidly changing job markets highlight the growing need for continuous and targeted skill development aligned with individual career goals. Existing course recommender systems in higher education predominantly rely on collaborative filtering or content-based techniques, and these systems struggle to match online courses with the targeted job skill demands due to their reliance on historical behavior data, resulting in issues such as the cold-start problem and limited adaptability to evolving career goals. To address these limitations, we introduce CRAFT as a course Recommender System (RS) for the German career context that integrates educational ontologies as a comprehensive domain knowledge and Large Language Models (LLMs) to provide personalized, career-oriented course recommendations along with clear explanations. In this way, CRAFT helps users continuously enhance their skills and make informed learning decisions. Experimental evaluations demonstrate CRAFT’s effectiveness in analyzing the skill gap and providing relevant recommendations supported by detailed explanations, increasing user satisfaction. This research contributes to personalized learning frameworks in education by providing personalized, continuous learning opportunities through effectively linking online courses with evolving occupational competencies.