Hi,I currently work on a GenAI platform for one of the largest local industrial companies. My daily work mostly involves building inference infrastructure on top of a 48x H200 GPU, Kubernetes and vLLM. Hence, I d say it s 80% SRE and 20% software engineering when it comes to building request routing and internal control planes.Although I have a background in backend engineering rather than ML research or low-level GPU programming, I am trying to understand what I need to learn to become a proper SWE, not just someone who knows how to deploy and serve LLMs. How did you get into AI infrastructure, and which skills made the biggest difference? I m especially curious about things you underestimated at first, such as distributed systems, useful resources along the way and difficult-to-acquire skills outside large-scale companies.Any answers or advice would be much appreciated.