Future Era: An LLM-Driven Career Planning Platform with Structured Roadmap Generation and Progress Tracking

Career planning can be challenging when a learner has a goal but is unsure which skills to develop, which resources to use, or how to organise the work over time. Relevant infor-mation is often spread across learning platforms, documentation sites, progress trackers, and job portals. This paper presents FutureEra, a web-based career-planning platform designed to bring these activities into one workflow. Users can enter a career goal and profile details, receive a structured roadmap generated with a large language model, explore milestone-related learning resources, record daily activity, compare career paths, use peer-benchmarking and community features, and interact with an AI mentor. The platform is implemented using React and Vite on the frontend, Node.js and Express.js on the backend, and MongoDB for persistent storage. Its generative features use the Groq API with an OpenAI GPT-OSS model selected through the server environment. The application also provides account management, password recovery, progress summaries, and PDF export. Generated roadmaps and estimates are presented as plan-ning suggestions, not as guarantees of salary, completion time, or employment. This paper describes the motivation, related work, system architecture, implementation, current limitations, and an evaluation plan. No quantitative performance or user-study results are claimed because verified experimental measurements are not available for this study.