International Journal of Scientific Research and Engineering Development

Recruitment is a critical activity for organizations seeking qualified employees for various job roles. In modern recruitment environments, companies often receive a large number of resumes for a single job opening, making manual resume screening a time-consuming and inefficient process. Human Resource (HR) professionals must analyze each resume individually to determine whether the candidate satisfies the job requirements. This process can lead to delays in recruitment and may also introduce inconsistencies due to subjective evaluation. This paper presents an AI Resume Shortlisting System that automates the initial screening stage of the recruitment process using machine learning and natural language processing techniques. The system analyzes candidate resumes and compares them with job descriptions to determine their relevance for a particular job role. Text preprocessing techniques are applied to extract meaningful information from resumes, and TF-IDF vectorization is used to convert textual data into numerical representations. Cosine similarity is then applied to measure the similarity between candidate profiles and job requirements. The proposed system is implemented as a web-based platform using Python, FastAPI, ReactJS, and MongoDB. The system enables HR professionals to upload multiple resumes simultaneously and automatically generates candidate evaluation results. Based on similarity scores, candidates are categorized into Shortlist, Hold, or Reject groups. The automated screening process significantly reduces recruitment time while maintaining consistent candidate evaluation.

Arun KS, Poorana Senthilkumar S, Srikar S,"AI Resume Shortlisting System Using Machine Learning" International Journal of Scientific Research and Engineering Development, V9(2): Page(335-340) Mar-Apr 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.