Abstract
Students and professionals today face significant challenges when navigating their career trajectories due to the extreme fragmentation of guidance tools. Existing platforms typically offer either scattered college searches, rigid keyword-based job portals, or static personality quizzes, leaving users without a cohesive, actionable pathway. To resolve this, we developed the AI Career System—a centralized, intelligent web application utilizing a 3-Tier MVC architecture. The system integrates five core modules: an AI Resume Builder using dynamic PDF rendering, a localized College Finder, a Real-Time Job Portal, a Machine Learning-powered Career Recommendation engine, and a Generative AI Chatbot. By implementing Scikit-learn for TF-IDF vectorization and Cosine Similarity, the system shifts from basic keyword querying to mathematical intent-matching. System testing demonstrated high accuracy in career matching and sub-second rendering speeds for dynamic PDF generation, proving that combining local Machine Learning with external Generative APIs creates a highly effective, unified career guidance ecosystem.
Keywords
Artificial IntelligenceMachine LearningFlaskScikit-learnGenerative AICareer GuidanceNatural Language Processing.
Cite this article
Faizan Malikshah Naikwadi, Adinath Subhash Patil, Diksha Santosh Bansode, Umesh Anandrao Patil, Tushar Tanaji Keskar (2026). Ai Career Recommendation System. International Innovations & Scholarly Trends Journal, 2(4), 415–418. https://doi.org/10.5281/zenodo.19638376
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