GenAI-Powered ATS: Enhancing Recruitment with Skill Fitment Analysis

Authors

  • Dr. J. M. Patil Associate Professor, Dept. of CSE, Shri Sant Gajanan Maharaj College of Engineering, Shegaon, India. Author
  • Krishna R. Kolekar UG Scholar, Dept. of CSE, Shri Sant Gajanan Maharaj College of Engineering, Shegaon, India. Author
  • Samarth V. Zamre UG Scholar, Dept. of CSE, Shri Sant Gajanan Maharaj College of Engineering, Shegaon, India. Author
  • Aniket S. Galzalwar UG Scholar, Dept. of CSE, Shri Sant Gajanan Maharaj College of Engineering, Shegaon, India. Author
  • Swastik P. Chaudhary UG Scholar, Dept. of CSE, Shri Sant Gajanan Maharaj College of Engineering, Shegaon, India. Author

DOI:

https://doi.org/10.47392/IRJAEH.2025.0159

Keywords:

Retrieval Augmented Generation (RAG), Large Language Models (LLM), Generative AI, Artificial Intelligence (AI), Application Tracking System

Abstract

This study introduces a novel candidate-job matching framework leveraging Generative AI (GenAI) as an alternative to conventional Natural Language Processing (NLP) techniques. While existing research predominantly focuses on NLP-based resume parsing and keyword matching, our methodology utilizes GenAI to produce comprehensive skill assessments by evaluating candidate qualifications against job descriptions. The framework extracts candidate profiles from CVs and analyses their alignment with job requirements, classifying proficiency levels into four tiers: Beginner, Intermediate, Competent, or Expert. This GenAI-driven approach not only demonstrates higher matching accuracy compared to traditional methods but also provides actionable insights for both candidates and hiring managers, thereby streamlining recruitment workflows.

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Published

2025-03-28

How to Cite

GenAI-Powered ATS: Enhancing Recruitment with Skill Fitment Analysis . (2025). International Research Journal on Advanced Engineering Hub (IRJAEH), 3(03), 1106-1110. https://doi.org/10.47392/IRJAEH.2025.0159

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