The interview was primarily focused on Generative AI concepts and their practical applications. Questions covered Large Language Models (LLMs), prompt engineering, Retrieval-Augmented Generation (RAG), vector databases, embeddings, fine-tuning techniques, and AI agent frameworks. The interviewer also asked scenario-based questions on designing GenAI solutions, selecting appropriate architectures for different use cases, handling hallucinations, evaluating model performance, and addressing scalability, security, and deployment challenges. Additionally, there were discussions around real-world implementation of GenAI applications, model optimization, and best practices for building production-ready AI systems.

About the role you were applying

Classical ML, statistics and LLMs