International Journal of Advanced Research in Computer and Communication Engineering

Abstract: In today’s highly competitive job market, organizations receive an overwhelming number of resumes for each job opening, making manual resume screening inefficient, time-consuming, and prone to human bias. Traditional recruitment methods rely heavily on keyword-based filtering and manual shortlisting, which often leads to inconsistent evaluations and missed opportunities to identify suitable candidates. To address these challenges, this paper presents an AI-Powered Resume Analyzer, an intelligent recruitment support system that automates resume parsing, skill extraction, candidate evaluation, and job–role matching using Artificial Intelligence (AI), Natural Language Processing (NLP), and Machine Learning (ML) techniques.
The proposed system analyzes resumes uploaded in digital formats and extracts structured information such as skills, education, experience, certifications, and personal details. Machine learning models evaluate resumes against predefined criteria and job requirements to generate suitability scores, rank candidates, and provide actionable insights such as skill gaps and improvement recommendations. The system is developed using Python, NLP libraries, machine learning frameworks, and a web-based interface, ensuring scalability, accuracy, and usability. Experimental evaluation demonstrates that the system significantly reduces resume screening time while improving recruitment accuracy and fairness. The AI Resume Analyzer offers a practical, scalable, and efficient solution for modern recruitment automation.

Keywords: AI Resume Analyzer, Recruitment Automation, Natural Language Processing, Machine Learning, Resume Parsing, Intelligent Hiring Systems

Downloads: | DOI: 10.17148/IJARCCE.2026.15188