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@article{198052,
        author = {Manikandan A and Rahul A and Nidish M and Balaganesan M},
        title = {AI-Powered Resume Builder with ATS Optimization Using Natural Language Processing and Generative AI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {9067-9071},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198052},
        abstract = {In the modern recruitment ecosystem, Applicant Tracking Systems (ATS) play a crucial role in filtering and ranking resumes based on keyword relevance, formatting, and job-specific requirements. However, many candidates fail to pass ATS screening due to lack of awareness about optimization techniques. This paper proposes an AI-powered Resume Builder that integrates ATS score analysis, Natural Language Processing (NLP), and Generative AI to enhance resume quality. The system enables users to create, upload, and optimize resumes while providing real-time ATS scoring and actionable feedback. It also supports automated content generation such as professional summaries and cover letters. Experimental results indicate a significant improvement in ATS scores and resume effectiveness after AI-based enhancement. The system reduces manual effort, improves user experience, and increases the probability of job selection in competitive environments.},
        keywords = {Applicant Tracking System, Artificial Intelligence, Natural Language Processing, Resume Optimization, Generative AI},
        month = {April},
        }