AI Resume Builder and Career Recommendation System

This research introduces a dual-module framework comprising an AI-driven Resume Optimization Engine and a Semantic Career Recommendation System. The system architecture employs a high-fidelity AI Resume Builder that utilizes Natural Language Processing (NLP) for automated keyword optimization. Beyond static document generation, the framework features a Predictive Career Recommendation Engine that analyzes the semantic features of the user's profile to suggest personalized professional trajectories. Crucially, the system implements a Real-time API-driven Matching Module that filters live job and internship listings by calculating the Cosine Similarity between candidate resumes and specific job descriptions (JDs). By facilitating a direct transition from profile building to opportunistic placement, the proposed system minimizes the informational asymmetry in the entry-level labor market.