AI INTERVIEW APP WITH FOCUS ON SOFTWARE ENGINEERING POSITIONS

Abstract

This thesis presents Entervue, a mobile application to assist job seekers in preparing for software engineering interviews. The app addresses a common gap in technical interview preparation: the lack of tools that simultaneously develop both technical and soft skills. Entervue integrates large language models (LLMs), speech-to-text, and text-to-speech technologies to simulate interactive interviews. It also offers real-time feedback and sentiment analysis. The application includes customizable difficulty levels, voice-based interactions, and progress tracking. Additionally, the app ensures secure data handling and responsive user experience. It was developed using FlutterFlow for the frontend and Firebase for backend services. There are two modes of the interview—Training and Real Interview—in order to divide skill enhancement and self-assessment. During evaluation through user testing and user feedback, the app’s usability, effectiveness, and potential for broader adoption was confirmed. Entervue is a scalable, intelligent interview preparation tool suitable for the evolving demands of the interview preparation market.