Democratizing Applicant Tracking Systems: An LLM-Driven Architecture for Real-Time Resume Optimization, ATS Scoring, and Semantic Alignment Evaluation

Copyright & License

Copyright © 2026 Authors retain the copyright of this article. This article 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.

BibTeX

@article{197558,
        author = {Shubham Bhardwaj and Nikhil Pratap Singh and Rajpal Nishad and Pallavi Rawat},
        title = {Democratizing Applicant Tracking Systems: An LLM-Driven Architecture for Real-Time Resume Optimization, ATS Scoring, and Semantic Alignment Evaluation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {7582-7589},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197558},
        abstract = {},
        keywords = {Applicant Tracking Systems, Resume Optimization, Large Language Models, Gemini, Natural Language Processing, Semantic Alignment, Document Parsing, ATS Scoring, Keyword Analysis, Resume Builder, Information Extraction, Generative AI},
        month = {April},
        }

Cite This Article

Bhardwaj, S., & Singh, N. P., & Nishad, R., & Rawat, P. (2026). Democratizing Applicant Tracking Systems: An LLM-Driven Architecture for Real-Time Resume Optimization, ATS Scoring, and Semantic Alignment Evaluation. International Journal of Innovative Research in Technology (IJIRT), 12(11), 7582–7589.

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