I have a technical interview coming up for a cloud role at a company focused on security and posture management. I currently work as a cloud administrator, and I was told the interview will focus on AI, but that description is pretty broad. I'm not sure whether they mean supporting the infrastructure that AI applications run on, developing models and applications, or something else.
At my current job, a custom AI model runs on EC2 instances, but my responsibilities are limited to the infrastructure. I create and manage the virtual machines, troubleshoot access and performance issues, and handle the underlying environment. I'm not involved with the application, model, or development work.
For people who have interviewed for similar cloud or infrastructure roles, what has "AI" meant in practice? I'd like to focus my preparation on the most relevant topics without assuming I need to become a machine-learning engineer.
4 Answers
For an infrastructure position, I’d prepare for two main areas. First is operating and governing AI services: hosting models, securing data, protecting sensitive prompts and outputs, controlling access, monitoring usage, and managing costs across providers. Review services such as managed model platforms, model-hosting patterns, networking, logging, and the difference between using a hosted API and running workloads on your own compute.
The second area is practical AI usage. They may ask how you use AI in your current job, how you verify its output, where it helps with troubleshooting or automation, and how you prevent it from exposing company information. You probably don’t need to explain how to train a model, but you should be able to discuss the operational concerns around running one.
The first thing I’d do is ask the recruiter or hiring contact for clarification. “AI-focused” is too vague to tell you whether they mean infrastructure, governance, application development, or simply how you use AI in your daily work. You don’t need to ask for the exact interview questions—just ask which areas of AI are relevant to the role so you can prepare appropriately.
That makes sense. I was worried they might not want to disclose much, but the scope is broad enough that asking for the general areas they expect seems reasonable.
Some interviews use “AI” less as a technical specialty and more as a way to learn how you think about the future of infrastructure. Questions might include how AI could change your role over the next few years, how you’re keeping your skills current, and how you currently use AI tools at work. Have a few specific examples ready, including how you validate answers and avoid blindly applying generated configurations.
I’ve seen this too. The questions can be surprisingly general, so it’s worth preparing both a practical example from your current job and a thoughtful opinion about how AI will affect cloud administration.
It may also be worth learning the basic terminology around AI agents, tool-using workflows, developer assistants, and Model Context Protocol servers. The important distinction is between asking a chatbot a one-off question and giving an AI controlled access to systems, data, or tools. In an infrastructure role, that raises questions about permissions, approval gates, audit logs, data boundaries, and safe automation.
You don’t need to pretend to have experience you don’t have. Be clear about what you manage today, then explain how you would approach securing, monitoring, and operating an AI workload with the right development and machine-learning specialists involved.
That’s helpful. My experience is mostly creating the EC2 environments, fixing login and performance problems, and managing costs, so I’ll focus on how those responsibilities apply to AI workloads rather than trying to learn model development from scratch.