GitBridge Careers is an AI-powered career assessment platform designed to evaluate and enhance the professional readiness of students and aspiring developers. Traditional resumes primarily depend on self-declared skills and may not accurately represent an individual\'s practical coding abilities. To address this limitation, the proposed system analyzes a user\'s GitHub profile, repositories, coding projects, and resume to provide a comprehensive evaluation of their technical skills and development activities. The platform uses AI-based analysis to generate a performance score and an engaging personalized roast that highlights the user\'s strengths, weaknesses, and areas requiring improvement. Furthermore, it provides customized recommendations for improving both the GitHub profile and resume, while identifying potential skill gaps. Based on the overall assessment, the system recommends relevant internships and job opportunities that align with the user\'s demonstrated skills and project performance. The proposed platform aims to encourage continuous learning, improve students\' professional portfolios, enhance career readiness, and assist them in identifying suitable career opportunities through a more practical and data-driven assessment approach.
Introduction
GitBridge Careers is an AI-powered career assessment platform designed to help students and aspiring software developers evaluate their technical skills and improve their career readiness. Traditional resumes often depend on self-reported skills and may not accurately represent a candidate’s practical programming abilities. The proposed system addresses this issue by analyzing GitHub profiles, repositories, projects, and resumes to provide a more comprehensive assessment of a developer’s capabilities.
The literature survey reveals that existing systems primarily focus on resume screening, job recommendations, and candidate-job matching. However, many lack detailed GitHub repository analysis, practical coding skill evaluation, integrated resume assessment, and personalized career development guidance. GitBridge Careers aims to bridge these gaps by combining practical coding evidence with professional resume information in a single platform.
The main objectives of the system are to analyze resumes using Applicant Tracking System (ATS) techniques, evaluate GitHub repositories, identify programming languages and technical specializations, calculate performance scores, and provide personalized feedback. It also identifies skill gaps, recommends relevant learning opportunities, and helps students discover suitable internships and jobs.
The system follows a layered architecture consisting of a frontend developed using React.js and Vite, a backend built with Python and FastAPI, and a MongoDB database. It integrates resume analysis, GitHub data collection, AI-based assessment, and career recommendation modules to process user information and generate useful insights.
The platform supports three types of users:
Students: Create professional profiles, upload resumes, connect GitHub information, review skill assessments, explore internships, and manage applications.
Recruiters: Create internship opportunities and review student profiles and applications.
Administrators: Manage users, recruiters, opportunities, applications, and overall platform activities.
The working process begins with user authentication and role identification. Students provide their resumes and GitHub details, which are processed to extract skills, education, experience, projects, and repository activity. The assessment module then identifies strengths, weaknesses, potential skill gaps, and technical specialization, such as Frontend, Backend, Full-Stack Development, Artificial Intelligence/Machine Learning, or DevOps. Finally, the system presents performance scores, improvement suggestions, and relevant career opportunities through a user-friendly dashboard.
An additional feature is an AI-generated roast that presents weaknesses and improvement areas in an engaging manner, encouraging users to improve their resumes, GitHub profiles, and technical portfolios.
Conclusion
GitBridge Careers provides a centralized platform for connecting students and recruiters by considering technical skills, projects, resume information, and GitHub profiles rather than relying only on traditional resume-based screening. The system supports three major roles—Student, Recruiter, and Admin—with features for profile management, internship discovery, opportunity management, applications, and candidate evaluation.
The project aims to reduce the gap between student capabilities and recruiter requirements by providing a structured platform where students can showcase their practical development experience and recruiters can evaluate candidates using broader profile information. Overall, GitBridge Careers demonstrates how a technology-driven career platform can make the internship and recruitment process more organized, accessible, and focused on practical skills.
References
[1] “Career Craft AI: A Personalized Resume Analysis and Job Recommendations System,” in 2024 1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics, and Smart Systems (ISTEMS), 2024, doi: 10.1109/ISTEMS60181.2024.10560126. (IEEE Xplore)
[2] “An Advanced Real-Time Job Recommendation System and Resume Analyser,” in 2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), 2023, doi: 10.1109/ICSSAS57918.2023.10331788. (IEEE Xplore)
[3] D. Mhamdi, R. Moulouki, M. Y. El Ghoumari, M. Azzouazi, and L. Moussaid, “Job Recommendation based on Job Profile Clustering and Job Seeker Behavior,” Procedia Computer Science, vol. 175, pp. 695–699, 2020, doi: 10.1016/j.procs.2020.07.102. (ScienceDirect)
[4] S. Yang, M. Korayem, K. AlJadda, T. Grainger, and S. Natarajan, “Combining content-based and collaborative filtering for job recommendation system: A cost-sensitive Statistical Relational Learning approach,” Knowledge-Based Systems, vol. 136, pp. 37–45, 2017, doi: 10.1016/j.knosys.2017.08.017. (ScienceDirect)
[5] C. Zhu, H. Zhu, H. Xiong, C. Ma, F. Xie, P. Ding, and P. Li, “Person-Job Fit: Adapting the Right Talent for the Right Job with Joint Representation Learning,” ACM Transactions on Management Information Systems, vol. 9, no. 3, 2018, doi: 10.1145/3234465. (EBSCO OpenURL)
[6] C. Zhu, H. Zhu, H. Xiong, et al., “Enhancing Person-Job Fit for Talent Recruitment,” in Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, 2018, pp. 25–34, doi: 10.1145/3209978.3210025. (DOI)
[7] Y. Huang, D.-R. Liu, and S.-J. Lee, “Talent recommendation based on attentive deep neural network and implicit relationships of resumes,” Information Processing & Management, vol. 60, no. 4, Art. no. 103357, 2023, doi: 10.1016/j.ipm.2023.103357. (NYMCTU Academic Hub)
[8] M. Kaya and T. Bogers, “An Exploration of Sentence-Pair Classification for Algorithmic Recruiting,” in Proceedings of the 17th ACM Conference on Recommender Systems (RecSys 2023), 2023, pp. 1175–1179, doi: 10.1145/3604915.3610657. (ITU Researcher)
[9] Z. Gong, Y. Song, T. Zhang, J.-R. Wen, D. Zhao, and R. Yan, “Your Career Path Matters in Person-Job Fit,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 8, pp. 8427–8435, 2024, doi: 10.1609/aaai.v38i8.28685. (AAAI Publications)
[10] C. Zhu, X. Hu, H. Wu, C. Qin, H. Zhu, and H. Xiong, “Enhancing job recommendations with LLM-based resume completion: A behavior-denoised alignment approach,” Information Processing & Management, vol. 62, no. 6, Art. no. 104261, 2025, doi: 10.1016/j.ipm.2025.104261. (ScienceDirect)
[11] “Skills2Job: A recommender system that encodes job offer embeddings on graph databases,” Applied Soft Computing, vol. 101, Art. no. 107049, 2021, doi: 10.1016/j.asoc.2020.107049. (ScienceDirect)
[12] “Development of a Recommendation System for Resume Tracking and Job Suggestions,” in 2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON), 2024, doi: 10.1109/DELCON64804.2024.10866841. (IEEE Xplore)
[13] GitHub Inc., “GitHub REST API Documentation,” GitHub Docs. GitHub REST API Documentation
[14] S. Ramírez, “FastAPI Documentation,” FastAPI. FastAPI Documentation
[15] MongoDB Inc., “MongoDB Documentation,” MongoDB Documentation. MongoDB Documentation
[16] Meta and React Contributors, “React Documentation,” React. React Documentation
[17] Vite Contributors, “Vite Documentation,” Vite. Vite Documentation
[18] Python Software Foundation, “Python Documentation,” Python Documentation. Python Documentation
[19] M. Jones, J. Bradley, and N. Sakimura, “JSON Web Token (JWT),” RFC 7519, Internet Engineering Task Force, 2015. (RFC Editor)
[20] OpenAPI Initiative, “OpenAPI Specification,” OpenAPI Initiative. OpenAPI Specification One important thing for your paper