AI-Powered Recruitment Hub: A Comprehensive Framework for Intelligent Talent Acquisition Using Machine Learning and Natural Language Processing

AI-Powered Recruitment Hub: A Comprehensive Framework for Intelligent Talent Acquisition Using Machine Learning and Natural Language Processing

Mr.Muthuramalingam, Assistant Prof, SMVIT,

Akrit Kumar, Student, SMVIT,

Kaushal Suryawanshi, Student, SMVIT,

Md. Sharique Ayub, Student, SMVIT

Abstract—

The contemporary recruitment landscape faces significant challenges including prolonged hiring cycles, unconscious bias, and limited scalability in traditional applicant tracking systems. This paper presents a novel AI-Powered Recruitment Hub that integrates Natural Language Processing (NLP) and Machine Learning (ML) algorithms to automate and optimize key recruitment functions. Our system incorporates intelligent resume parsing, sentiment- based interview analysis, and predictive job-candidate matching to enhance both recruiter productivity and candidate experience. The platform demonstrates a 60% reduction in time-to-hire while improving candidate relevance through data-driven screening mechanisms. Built using a modular Node.js backend with MongoDB for scalable document storage, and a React frontend, the system leverages BERT-based contextual embeddings for semantic resume analysis and sentiment classification models for interview evaluation. Experimental validation on a dataset of 2,500 resumes across 15 job categories shows 85.7% accuracy in candidate- job matching, with significant improvements in fairness metrics compared to conventional systems. The findings contribute to the advancement of ethical AI in human resources, providing a scalable framework that addresses modern workforce needs while maintaining transparency and reducing algorithmic bias.

Index Terms—Artificial Intelligence, Natural Language Processing, Machine Learning, Recruitment Automation, Human Resources, Sentiment Analysis, Job Matching