Smart Job Portal With AI-Powered Skill Matching, ATS Scoring and Intelligent Career Assistance using Django Framework

P. Hema, B. Shireesha

Smart Job Portal; ATS Score; Skill Matching; Artificial Intelligence; Resume Builder; Django; Recruitment Automation.

The rapid expansion of digital recruitment platforms has intensified the challenges associated with efficient candidate-job matching, manual resume screening, and lack of personalized career guidance. This paper presents a Smart Job Portal with AI-Powered Skill Matching and Career Assistance, a full-stack web-based system designed to streamline recruitment processes and enhance candidate experience through intelligent automation. The system is developed using the Django framework with Python as the core programming language and SQLite as the backend database. It incorporates an advanced Applicant Tracking System (ATS) that computes match scores by comparing candidate skills from both profile data and resume text against job requirements. The system further integrates AI-powered resume advice and chatbot assistance using large language model APIs such as OpenAI and Google Gemini. The platform supports multi-role access for candidates and HR professionals, providing dedicated dashboards and workflows. Candidates can build resumes, apply for jobs, track applications, and receive personalized suggestions, while recruiters can post jobs, evaluate applicants using ATS scores, and manage interview scheduling within a unified system. Experimental evaluation demonstrates improved recruitment efficiency, reduced manual effort, and enhanced candidate engagement. The proposed system offers a scalable and intelligent alternative to traditional job portals by combining automation, artificial intelligence, and full-stack development principles.

[1] Django Software Foundation, Django Documentation. [Online]. Available: https://docs.djangoproject.com/.
[2] OpenAI, OpenAI API Documentation. [Online]. Available: https://platform.openai.com/docs/.
[3] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” in Proc. 2019 Conf. North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Minneapolis, MN, USA, Jun. 2019, pp. 4171–4186.
[4] T. M. Mitchell, Machine Learning. New York, NY, USA: McGraw-Hill, 1997.
[5] Scikit-learn Developers, Scikit-learn Documentation. [Online]. Available: https://scikit-learn.org/stable/documentation.html..
[6] LinkedIn Corporation, LinkedIn Developer Documentation. [Online]. Available: https://learn.microsoft.com/linkedin/.
[7] Indeed, Indeed Hiring Lab: Labor Market Research and Hiring Trends. [Online]. Available: https://www.hiringlab.org/.
[8] Info Edge (India) Ltd., Naukri JobSpeak Report. [Online]. Available: https://www.naukri.com/jobsspeak.
[9] Bootstrap Team, Bootstrap Documentation. [Online]. Available: https://getbootstrap.com/docs/.
[10] SQLite Consortium, SQLite Documentation. [Online]. Available: https://www.sqlite.org/docs.html.
[11] Python Software Foundation, Python Documentation. [Online]. Available: https://docs.python.org/3/.
[12] Google, TensorFlow Documentation. [Online]. Available: https://www.tensorflow.org/api_docs.