Smart Career Path Recommender

Abstract— In today’s dynamic job market, students and early-career professionals often face challenges in identifying suitable career paths, analyzing skill gaps, and preparing effectively for employment. This project introduces the Smart Career Path Recommender, an AI-driven web platform that provides personalized career guidance through adaptive mentorship and real-time skill gap analysis. The system leverages Large Language Models (LLMs) such as LLaMA and Gemini AI, alongside Retrieval-Augmented Generation (RAG) and semantic search via vector embeddings (Ollama + ChromaDB), to offer dynamic, context-aware recommendations. Key features include domain-specific quizzes for skill assessment, automatic generation of resumes and cover letters tailored to job roles, and personalized interview preparation modules. Natural Language Processing (NLP) techniques using SpaCy are employed for accurate keyword extraction and query understanding. The platform supports multimodal interactions including voice input, with future support planned for multilingual and offline access. Experimental evaluations demonstrate high user satisfaction, improved resume relevance (Jaccard similarity: 0.41), and a 23% improvement in skill assessment scores. With its modular architecture and inclusive design, the platform addresses critical gaps in conventional career counseling and empowers users to make informed, confident, and data-driven career decisions. Keywords— Artificial Intelligence (AI), Career Guidance, Skill Gap Analysis, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Gemini AI, Natural Language Processing (NLP), Semantic Search