Smart Career Platform: An AI-Driven Personalised Career Guidance System for Multi-Domain Professionals Using MERN Stack and Groq Large Language Models

Abstract: The proliferation of digital career platforms has disproportionately focused on technology professionals, leaving practitioners in law, medicine, finance, design, marketing, and education without adequate AI-driven career guidance. This paper presents the Smart Career Platform — a comprehensive full-stack career development system built on the MERN stack (MongoDB, Express.js, React.js, Node.js) and integrated with Groq's Llama 3.1 Large Language Model (LLM). The platform introduces a novel field-first personalisation architecture that propagates rich user context across all career guidance features: adaptive skill assessments, AI-generated career roadmaps, ATS-optimised resume building, AI mock interview practice, and a multi-session advisory chatbot. A four-step onboarding mechanism captures five context dimensions (field, user type, career goal, target role, and experience level) and injects this context into every AI prompt via system prompt engineering. The assessment engine employs a hybrid strategy: a curated static question bank for 12 established domains and dynamic AI-generated questions for unlimited novel domains. Deployed in production on Vercel and Render with MongoDB Atlas, the platform supports 10+ professional fields and 22+ career roles, achieving sub-4-second AI response times. Empirical comparative analysis demonstrates significant advantages over five leading career platforms across seven key capability dimensions. The system validates that prompt-engineered context injection is a cost-effective alternative to domain fine-tuning for specialised AI advisory applications.

Keywords: Artificial Intelligence, Career Guidance, MERN Stack, Groq AI, Llama 3.1, Large Language Model, Personalised Learning, ATS Resume Builder, Skill Assessment, React.js, MongoDB, Field-First Personalisation, Full- Stack Development.