Automated Recruitment System

Abstract: The growing demand for efficient and scalable hiring processes has led to the adoption of intelligent automation in recruitment systems. Conventional recruitment methods, such as manual screening and traditional interviews, fail to provide consistent, unbiased, and real-time evaluation of candidate performance. This paper presents the design and development of an AI-powered automated recruitment system using Large Language Model (LLM) integration. The proposed system utilizes a full-stack architecture with Next.js, React, and PostgreSQL, integrated with LLM APIs for intelligent question generation and candidate evaluation. Candidates interact with an AI chatbot through text and voice-based communication, along with an integrated coding environment for technical assessments. The evaluation engine generates feedback, ratings, and hiring recommendations. Experimental validation demonstrates consistent performance and efficient automation of recruitment workflows.

Keywords: AI Recruitment, LLM, Automated Interview System, NLP, Full Stack Development, Candidate Evaluation, Next.js, Supabase, Role-Based Access Control