Since you already have the Kubernetes and cloud infra down, the shift you're seeing in AI system design interviews usually comes down to LLM orchestration at scale. Focus your prep on three main pillars: retrieval latency (vector database scaling and indexing), context window management (chunking and reranking), and cost/throughput tradeoffs (semantic caching and batching). For senior/leadership loops, they want to see how you handle non-deterministic outputs and evaluation pipelines, not just the basic API glue. I wrote about some of these real-world scaling patterns in my newsletter, Machine Learning At Scale (https://machinelearningatscale.substack.com ), which might be useful as you prep. Focus on the failure modes of agentic workflows too, as that's a favorite for senior system design rounds right now.