Automated interviewers and chatbots are common in research, recruitment, customer service, and education. Many existing systems use fixed question lists, strict rules, and limited personalization, leading to repeated conversations that cause low engagement. Therefore, these tools are not effective for complex qualitative research, which requires flexibility, context awareness, and ethical sensitivity. Consequently, there is a need for a more adaptive and context-aware interviewing system. To address this, an AI-powered interviewer that dynamically generates questions that are contextually appropriate and expertise aligned is presented in this study. The interviewer is built on a locally hosted large language model (LLM) that generates coherent dialogue while preserving data privacy. The interviewer profiles the participants’ expertise in real time to generate knowledge-appropriate questions, well-articulated responses, and smooth transition messages similar to human-like interviews. To implement these functionalities, a modular prompt engineering pipeline was designed to ensure that the interview conversation remains scalable, adaptive, and semantically rich. To evaluate the AI-powered interviewer, it was tested with various participants, and it achieved high satisfaction (mean 4.45) and engagement (mean 4.33). The proposed interviewer is a scalable, privacy-conscious solution that advances AI-assisted qualitative data collection.