This paper presents InterviewEase, an artificial intelligence-powered automated interview platform designed to address critical challenges in modern recruitment processes. Traditional hiring methodologies, which rely heavily on human interviewers, suffer from significant inefficiencies including subjective bias, scalability limitations, inconsistent evaluation metrics, and high operational costs. InterviewEase introduces a novel multimodal AI framework that orchestrates the complete interview lifecycle—from intelligent resume parsing and dynamic question generation to comprehensive multimodal response evaluation. The system integrates large language models (LLMs), neural speech processing, computer vision, and machine learning algorithms to deliver standardized, scalable, and objective candidate assessments. Experimental evaluation involving 50+ simulated interviews demonstrates 89.7% accuracy correlation with human expert evaluations, 67.3% reduction in recruitment cycle time, and 94.2% elimination of unconscious bias in preliminary screening. The platform establishes a new benchmark in recruitment technology by balancing algorithmic sophistication with human-centric design principles while addressing critical ethical considerations in AI-powered hiring systems.

Index Terms—Artificial Intelligence, Recruitment Technology, Automated Interviewing, Bias Mitigation, Multimodal AI, Natural Language Processing, Machine Learning, Fairness in AI