Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{185215,
        author = {Dr M.K. Jayanthi Kannan and Paarth Juneja and Manav Tiwari and Kartik Modi and Sankalp Agnihotri and Sarthak Tiwari and Hardik Jain},
        title = {ResumeBERT-HireNet: An Intelligent Agentic Framework for Autonomous Recruitment Optimization},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {5},
        pages = {994-1007},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=185215},
        abstract = {Traditional recruitment processes often reject up to 75% of resumes before human review, resulting in missed opportunities and increased workloads for hiring teams. The rapid growth of job applications and recruiter workloads has intensified the demand for intelligent recruitment optimization systems. This paper introduces ResumeBERT-HireNet, an Agentic-AI powered recruitment framework that leverages Large Language Models (LLMs), deep contextual embeddings, blockchain validation, and quantum-enabled optimization for autonomous candidate-job alignment. The system utilizes ResumeBERT for semantic resume parsing, a Recruiter Agent for dynamic requirement mapping, and a Quantum Optimizer to resolve large-scale candidate-job pairing challenges efficiently. Additionally, a Trust Agent ensures authenticity through blockchain-based credential verification. Experimental analysis demonstrates that ResumeBERT-HireNet improves resume parsing accuracy by 18%, reduces recruiter workload by 35%, and optimizes candidate-job matching with near real-time performance. This framework contributes to enhancing fairness, transparency, and efficiency in the recruitment ecosystem. This paper presents an AI-powered recruitment platform that uses advanced NLP to convert unstructured resumes into structured data. The main objectives are to provide AI AI-powered recruitment Framework for Intelligent Resume Matching and Candidate Profiling, bridging the Gap Between Job Seekers and Recruiters Through Context-Aware Matching, an AI Ecosystem for Automated Resume Parsing, Skill Mapping, and Recruiter Discovery, ResumeBERT-HireNet: An Intelligent Agentic Framework for Autonomous Recruitment Optimization, AI for Contextual Resume-Job Alignment and Talent Discovery. Key features include automated resume parsing, AI-driven scoring, semantic matching, and fairness-aware ranking, reducing bias and improving candidate-job fit. With dynamic dashboards and tailored feedback for job seekers and employers, the platform streamlines hiring, promotes diversity, and enhances the overall recruitment experience.},
        keywords = {AI Recruitment, Resume Parsing, ResumeBERT, Agentic AI, Quantum Optimization, Recruitment Automation, Blockchain Validation, Resume Parsing Semantic Matching, Candidate Ranking & Scoring, Fairness-aware, AI Applicant Tracking System (ATS), Natural Language Processing (NLP), Named Entity Recognition (NER).},
        month = {October},
        }