1. Can you describe the architecture of a recent ML project, including data flow, pipeline components, model deployment strategy, and how you ensured scalability, performance, and monitoring post-deployment? 2. How would you design a real-time machine learning system that supports low-latency predictions, high availability, and integration with business APIs while handling continuously growing data efficiently? 3. Explain how you would architect a Retrieval-Augmented Generation (RAG) system combining a vector store, retriever, and language model to serve accurate responses in a scalable production environment. 4. When building a machine learning platform for multiple use cases, how do you modularize components like training, serving, and monitoring to allow team collaboration and faster iteration? 5. Describe your approach to choosing between cloud-native services and open-source tools when designing a machine learning system under constraints like budget, security, and deployment timelines. 6. How do you optimize model performance and resource utilization during training and inference, especially under constraints such as limited compute, large datasets, or strict latency requirements?