International Journal of Scientific Research and Engineering Development

Students and job seekers find it challenging to match their skills with rewarding careers due to rapid advancements in technology and complex career paths. Conventional career counseling systems relying on human reviews or fixed aptitude tests fail to consider psychological and experiential complexities. The study presents "Career Path Optimization using MBTI Profiling, Resume Analysis, and Competency Gap Detection", an AI-powered system designed to close this gap. To provide individualized, data-driven recommendations, it combines generative AI reasoning with machine learning pattern recognition [1][2].The process starts with an MBTI assessment [14] to map personality traits like introversion/extraversion and thinking/feeling to compatible career roles. Uploaded PDF or DOCX resumes are processed by an advanced analysis module using PDFMiner and spaCy [6][9] for named entity recognition and text extraction, creating structured profiles of education, experience, and skills (e.g., Python, SQL). Extracted skills are fed into a Decision Tree classifier [1] trained on job role datasets, predicting the top three suitable roles (e.g., “Data Scientist,” “ML Engineer”) with confidence scores using TF-IDF [7][8]. The Google Gemini 2.5 Flash API [25] refines predictions by combining MBTI and ML insights to generate JSON outputs, including a skill gap analysis, resume quality score (0–100), and personalized learning paths with Kaggle, Udemy, and Coursera links. An AI chatbot [19][20] enables ongoing interaction, while a Django-based dashboard [23] visualizes insights and exports professional reports. This integration of AI, ML, NLP, and behavioral profiling [4][5] provides a cutting-edge, personalized career counseling system suited to today’s evolving job market.