AI-Powered Applicant Tracking System: An Intelligent Approach to Modern Recruitment

Recruitment has become increasingly challenging due to the vast number of applications received for each job

opening. Traditional Applicant Tracking Systems (ATS) rely on keyword-based filtering, which often results in inaccurate

candidate-job matches. This paper proposes the development of an AI-Powered Applicant Tracking System (AI-ATS) that

integrates Artificial Intelligence (AI) and Natural Language Processing (NLP) to automate resume parsing, skill extraction, and

candidate-job matching. The system provides intelligent ranking, candidate feedback, and scalability for enterprise-level

deployment. Experimental evaluation suggests that AI-driven semantic analysis significantly improves the accuracy and

efficiency of recruitment processes.

The rapid growth of online recruitment platforms has resulted in a significant increase in the number of applicants for every job

opening, making manual resume screening an inefficient and error-prone process. Traditional Applicant Tracking Systems

(ATS) depend predominantly on keyword-based filtering techniques, which often overlook qualified candidates due to linguistic

variations, inconsistent resume formats, and lack of contextual understanding. This research proposes an AI-Powered Applicant

Tracking System (AI-ATS) that leverages recent advancements in Natural Language Processing (NLP), Machine Learning

(ML), and semantic embedding models to automate and enhance the hiring workflow.