Authors : B. Rajesh; Dr. S. Prakasam

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/34s8aajz

Scribd : https://tinyurl.com/bxtucnuj

DOI : https://doi.org/10.38124/ijisrt/26apr2217

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The digital transformation of recruitment processes has created a pressing need for intelligent, skill-aware jobmatching platforms. This paper presents JobHunter, a full-stack AI-powered job recommendation system built on the MERN (MongoDB, Express.js, React.js, Node.js) technology stack. The system integrates Google Gemini's large language model (LLM) API to automate job description generation, perform real-time external job aggregation, and enrich job data semantically. JobHunter provides dual-role functionality for both job seekers and employers: job seekers can manage profiles, track applications, and receive personalized job matches based on skills and experience, while employers can post roles, manage applicants, and leverage AI to generate compelling, SEO-optimized job descriptions. The platform incorporates a scheduled job-fetching mechanism using node-cron, an intelligent skill inference engine, and a cloud-based media pipeline via Cloudinary. Experimental observations indicate significant improvements in recruiter productivity and job-seeker engagement compared to traditional portals. JobHunter demonstrates how modern AI integration can meaningfully elevate the recruitment experience for all stakeholders.

Keywords : AI Job Recommendation, MERN Stack, Google Gemini API, Natural Language Processing, Recruitment Automation, Skill Matching, Full-Stack Web Application, OpenAI, Job Portal, Applicant Tracking System.

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