Authors: Radhika Kulkarni, Tejal Mungase
Abstract: The current job market introduces major difficulties in effectively connecting skilled candidates with suitable employment opportunities. This paper proposes an AI-powered career advisory system using Natural Language Processing (NLP), Machine Learning (ML), and Large Language Models (LLMs) to automate career guidance. The proposed system unites four core modules: semantic-based resume parsing using transformer models, intelligent job matching using BERT embeddings, complete skill gap analysis with customized learning recommendations, and AI-driven resume optimization for Applicant Tracking System (ATS) compatibility. The system implements a three-tier architecture with React.js frontend, FastAPI backend, and a hybrid database layer. This Stage 1 paper presents problem identification, literature survey, system architecture, and methodology. The framework addresses critical gaps in career advisory services through conceptual understanding, customized guidance, intelligent automation, and access to professional career counseling.
