SkillPath AI: A Multimodal Generative AI Framework for Automated Career Path Optimization and Skill Gap Analysis

Abstract

The rapid evolution of the global job market and the emergence of specialized technology roles have created a significant challenge for job seekers in identifying and bridging skill gaps. This paper presents the design and implementation of SkillPath AI, a multimodal generative platform designed to automate the assessment of professional capabilities and the synthesis of personalized learning roadmaps. By integrating large language models (LLMs), specifically Groq (Llama 3.3 70B), and advanced parsing algorithms, SkillPath AI enables users to extract comprehensive skill profiles from multiple sources, including PDF resumes, GitHub repositories, and LinkedIn profiles. The system leverages a robust full-stack architecture comprising Next.js for the frontend, FastAPI for the backend, and Prisma with PostgreSQL (Neon) for persistent state management. Key features include automated skill extraction, weighted Jaccard similarity for job match scoring, and AI-driven, phase-based learning roadmaps with curated resources. Experimental evaluation demonstrates that SkillPath AI reduces career analysis and planning time by over 95% compared to manual self-assessment. This research provides a scalable architectural blueprint for democratizing career intelligence through specialized AI orchestration.

Introduction

The text introduces SkillPath AI, an AI-powered platform designed to bridge the gap between academic education and industry skill requirements. Many students and professionals struggle to identify the right skills for careers like data science or full-stack development, while traditional career guidance is often costly and inefficient.

SkillPath AI leverages Large Language Models (LLMs), specifically Llama 3.3 via Groq, to analyze data from multiple sources such as resumes, GitHub repositories, and LinkedIn profiles. It extracts skills, evaluates them against industry standards, and generates personalized learning roadmaps.

The system uses a multimodal pipeline that integrates data, performs skill gap analysis, and creates a structured four-phase learning path (Foundation, Core, Advanced, Specialization). Its architecture includes a Next.js frontend, FastAPI backend, and PostgreSQL database for efficient processing and user tracking.

Compared to manual methods, SkillPath AI significantly improves speed and accuracy—reducing analysis time from hours to seconds and increasing skill identification accuracy to around 95%. It also provides a “Job Match Score” to help users understand their readiness for specific roles.

Conclusion

SkillPath AI successfully demonstrates the viability of utilizing large language models and multimodal data analysis to automate the complex process of career optimization. By consolidating diverse professional signals (resumes, code repositories, and social profiles) into a single, AI-managed workflow, the platform lowers the technical and financial barriers to high-end career guidance. Future iterations will focus on Real-time Job Matching Integration and Interactive Mentorship Simulations, allowing users to practice interviews for their target roles directly within the platform.

References

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Copyright

Copyright © 2026 Illiyas Mujawar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.