Career navigation in rapidly evolving technology sectors presents substantial cognitive and motivational challenges for learners at all levels. Traditional static roadmaps lack adaptability to individual progress, skill gaps, and shifting industry demands. This paper presents an Interactive Career Roadmap Website — a web-based platform that integrates structured career path visualization, real-time milestone progress tracking, and AI-guided personalized recommendations using a fine-tuned large language model (LLM). The system defines four primary technology career trajectories — Data Science, Full-Stack Development, Cybersecurity, and Machine Learning Engineering — decomposed into prerequisite-linked hierarchical milestones. Users log completed milestones; the AI engine dynamically recalculates next steps by reasoning over current skill assessments, learning velocity metrics, and live industry demand signals obtained via web search augmentation. A randomized controlled study involving 120 participants over eight weeks demonstrated a statistically significant 34% improvement in goal clarity (p < 0.01) and a 28% increase in self-reported learning motivation compared to static roadmap baselines. The paper details system architecture, the recommendation engine design, human-computer interaction principles applied, evaluation methodology, case studies, and open research directions for personalized career development platforms at scale.

Keywords— Career Roadmap, Progress Tracking, AI Recommendations, Skill Gap Analysis, Personalized Learning, Human-Computer Interaction, Large Language Models, Retrieval-Augmented Generation, EdTech, Knowledge Graphs.