I have an upcoming technical interview 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 include AI-related topics, but that feels pretty broad. I'm unsure whether they mean supporting the infrastructure that AI applications run on, deploying and governing AI services, or discussing application and model development. At my current job, a custom AI model runs on EC2 instances, but my responsibilities are limited to provisioning and managing the infrastructure, troubleshooting access and performance problems, and maintaining the virtual machines. I'm not involved with the model, application, or development work. For people who have interviewed for similar cloud or infrastructure roles, what has "AI" typically covered, and what should I study beforehand?
4 Answers
For an infrastructure role, there are probably two major areas. One is operating and governing AI services: hosting models, securing data, protecting sensitive information, controlling access, monitoring usage, and managing costs across different providers. The other is practical AI usage—how you use assistants or coding agents effectively, evaluate their output, structure workflows, and keep their use controlled inside an organization. You probably don’t need to become a model developer, but you should understand the major concepts and be able to explain how AI workloads differ from ordinary applications.
The first thing I’d do is ask the recruiter or hiring contact for clarification. “AI-focused” is broad enough to mean infrastructure, governance, application support, or simply general awareness. You don’t need to ask for the exact questions—just ask which areas the role is expected to own so you can prepare appropriately.
That makes sense. I was hesitant because I assumed they might not want to reveal much, but the description is vague enough that I probably should ask about the general scope.
It would be worthwhile to review the basics of AI infrastructure and operations: model hosting, inference versus training, data privacy, identity and access controls, logging, resource sizing, and cost management. You can also learn the general ideas behind AI agents, tool integrations, and structured workflows. Frame your existing experience honestly—you support the compute and operating environment, even if you don’t build the model itself.
Exactly. Don’t pretend to be an ML engineer if that isn’t your background. Explain what you have managed, then show that you understand where the application, model, data, and infrastructure responsibilities meet.
Some interviews use “AI” in a much less technical way. You may get questions about how AI could change infrastructure work, how you stay current as tools improve, and how you currently use AI in your job. Be ready to discuss a practical example, including what you used it for, how you checked the results, and what information you would never put into an AI tool.
I’ve encountered interviews where the AI questions were mostly about judgment and adoption rather than model engineering. The interviewer wanted to know whether candidates could use these tools responsibly and still verify their work.
That helps clarify the direction. My experience is mainly creating and maintaining the EC2 infrastructure used by an AI system, including access and performance troubleshooting, so I’ll focus on governance, security, cost, and the basics of how those workloads are deployed.