<p>In today’s rapidly evolving job market, personalized career development is essential for aligning individual skills with industry demands. This paper presents a comprehensive AI-driven system that provides tailored guidance for skill enhancement, course selection, and job matching, primarily in the technology sector. The system integrates five core components: resume parsing, resume classification, company-based skill recommendation, skill-based course recommendation, and job recommendation. Machine learning and deep learning models, including BERT and traditional classifiers, are used for resume classification with high accuracy. The skill extraction module combines multiple NLP models to enhance precision in identifying candidate competencies. A hybrid recommendation approach—combining collaborative filtering (ALS) and content-based methods—identifies skill gaps using LinkedIn data, while suggesting relevant courses from the Coursera dataset. Job recommendations are generated using real-time LinkedIn data streams, filtered by content similarity. The system employs a modular, scalable architecture that integrates real-time job data via Apache Kafka and PySpark Streaming, enabling continuous updates. Evaluation results demonstrate high model performance, including classification accuracy above 99% for several models and recommendation precision exceeding 80% in key subsystems. While the current implementation is focused on software-related roles, the framework is extensible to other industries with domain-specific data. This work presents a novel, end-to-end pipeline for AI-assisted career guidance that integrates static profile data with real-time labour market intelligence.</p>