AI-Powered Recruitment Tool: Resume Evaluator and ATS Integration
Mrs. Lithu Mathew
Asst. Professor Dept. ISE
AMC Engineering College
lithu.mathew@amceducation.in
Amrutha M
Dept. ISE
AMC Engineering College Bengaluru, India
Bengaluru, India
amruthamanjunath27@gmail.com
Abstract— The recruitment process is often hindered by inefficiencies such as time-intensive manual resume screening, duplicate candidate submissions, and the absence of a centralized tracking mechanism. These challenges not only delay hiring decisions but also impact the accuracy and consistency of candidate evaluation.
This project presents an AI-powered recruitment tool designed to address these issues by integrating two core modules: a Resume Evaluator and an Applicant Tracking System (ATS).
The Resume Evaluator allows users to upload a job description (JD) along with up to ten resumes simultaneously. Using natural language processing and semantic similarity algorithms, it ranks resume based on relevance to the JD. Users can customize evaluation criteria such as skills, experience, and keywords to meet specific hiring needs. The results are compiled into a downloadable PDF report, enabling faster and more data-driven shortlisting.
The ATS module focuses on operational efficiency by detecting and eliminating duplicate resumes, logging recruiter ownership, and maintaining a comprehensive, centralized candidate database. It also provides recruiter activity tracking, allowing hiring teams to monitor the number of resumes uploaded by each recruiter on a daily or periodic basis.
By automating resume evaluation and streamlining candidate tracking, this solution reduces time-to-hire, enhances matching precision, and improves recruiter productivity. Built on scalable cloud infrastructure and driven by AI capabilities, the system ensures adaptability, reliability, and seamless integration into modern recruitment workflows.
Keywords — Artificial Intelligence (AI), Resume Evaluation, Applicant Tracking System (ATS), Recruitment Automation, Natural Language Processing (NLP), Semantic Matching, Duplicate Detection, Candidate Database, Recruiter Performance Tracking, Hiring Efficiency
