• Muskan Chaturvedi
• Apurva Lakeshri
• Lalit Jajoo
• Raj Soni
• Chitra Wasnik

Abstract:- Career guidance is a vital aspect of personal and professional development, helping individuals navigate complex career decisions. However, traditional career counseling methods often fall short in providing personalized, adaptive, and up-to-date advice that aligns with rapidly evolving industry trends. This research aims to address these limitations by introducing an AI-powered career coaching system designed to empower users with tailored career development tools. Leveraging advanced technologies such as machine learning and natural language processing (NLP), the system can assess user profiles, analyze skills, and recommend career paths that align with individual strengths and aspirations. Real-time data analytics enhance the system’s ability to track industry trends, offering users insights into emerging roles, in-demand skills, and evolving market requirements. To ensure scalability and performance, the platform is built with modern web technologies like Next.js for fast, dynamic front-end experiences, Prisma as a robust ORM for efficient database management, and Neon DB for high-performance, cloud-native PostgreSQL databases. Additionally, Inngest powers background workflows and event-driven processes, allowing the system to handle complex tasks like resume evaluations, personalized learning path generation, and real-time notifications without compromising speed.

Keywords— Career Guidance, Artificial Intelligence, Machine Learning, Natural Language Processing, Real-Time Analytics, Resume Evaluation.

"AI-Powered Career Coaching System: A Data-Driven Approach to Career Guidance", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.f238-f244, March-2025, Available :http://www.jetir.org/papers/JETIR2503560.pdf

"AI-Powered Career Coaching System: A Data-Driven Approach to Career Guidance", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppf238-f244, March-2025, Available at : http://www.jetir.org/papers/JETIR2503560.pdf