DevOps Architect Interview Questions for AI Training Work

I design for horizontal scalability from the start, using managed services and auto-scaling groups rather than fixed-capacity resources, so growth is handled by adding capacity rather than re-architecting later. I still size the initial footprint to actual near-term need, since paying for headroom the product may never reach wastes budget that could fund other priorities.

I break infrastructure into modules with clear ownership boundaries, so a team changing their own service's resources doesn't need to touch a shared state file owned by another team. I also enforce peer review on infrastructure changes the same way we would on application code, since a bad infrastructure change can have a much wider blast radius.

I put anything that validates code correctness, tests, linting, security scanning, in the CI stage so problems surface before a build is even considered deployable. Deployment-time steps are reserved for actions that are genuinely tied to the target environment, like configuration injection or a smoke test against the live service.

I stage the migration behind a traffic-shifting mechanism, moving a small percentage of traffic first and watching key metrics before increasing the shift, rather than cutting over all at once. I keep a clear rollback path ready at each stage so a problem can be reversed quickly instead of forcing a rushed fix under pressure.

I run regular drift detection against the codified state and treat any manual change discovered that way as a signal to investigate why it happened, not just to revert it silently. If manual changes keep recurring in the same area, that usually means the code-based process is too slow or cumbersome for that specific need.

I'd look outside the application code first, at shared infrastructure, dependency versions, or environment changes, since intermittent failures with no code change usually point to something in the surrounding system rather than the service itself. I'd check whether the failure correlates with a specific time, load level, or recent platform update before assuming it's random.

I'd start with usage data to find resources that are over-provisioned relative to actual load, rather than cutting broadly across every service equally, since indiscriminate cuts risk hitting something load-bearing. Right-sizing instances and eliminating idle resources usually yields meaningful savings before any architectural tradeoff against reliability is even necessary.

I'd bring both teams into a conversation about the actual underlying need behind each request, since conflicting infrastructure requests often reveal a design that should be split into two separately configurable resources rather than one shared one. I'd rather solve the structural conflict than pick a winner and leave the other team blocked.

A change to auto-scaling thresholds meant to reduce cost caused a brief period of degraded performance during an unanticipated traffic spike. I rolled back the threshold change immediately, then re-approached it with a gradual adjustment tested against historical traffic patterns rather than a single aggressive change, which achieved the same savings without the risk.

A team was comfortable making quick manual changes directly in the cloud console and saw code-based infrastructure as unnecessary overhead. I showed them a recent incident caused by an undocumented manual change and proposed starting with just their most critical resources in code rather than a full migration, which made the value concrete without demanding an overwhelming upfront investment.