Senior AI Operations Engineer (Senior Software Engineer)
Posted Sep 20, 2026 · We last checked this listing on Oct 5, 2026
Likely interview questions for this role
Written from this job description, not a generic list. Each one notes what the interviewer is really checking.
About your past
Tell me about a reusable platform pattern you created that other teams adopted.
What they are checking: Raising engineering standards across the AI/ML team, which the role explicitly owns.
Tell me about a production incident you led. What did the postmortem change?
What they are checking: Ownership during incidents and follow-through on lasting fixes.
What would you do
An LLM-backed service is slow and occasionally times out under load. How do you troubleshoot?
What they are checking: Distinguishing model latency, rate limits and backend bottlenecks, and adding retries, queues or caching sensibly.
A data scientist wants direct production access to debug a model. How do you respond?
What they are checking: Protecting production while giving teams the observability and tools they need to work.
About the work
Walk me through how you'd productionize a RAG pipeline that a data scientist built in a notebook.
What they are checking: Turning prototypes into reliable services with APIs, scaling, monitoring and proper secrets handling.
How have you used Pulumi or another infrastructure-as-code tool, and how do you keep stacks maintainable?
What they are checking: Hands-on IaC depth, including modular design, environments and safe change management.
How do you design CI/CD for an AI application where prompts and models change alongside code?
What they are checking: Versioning and testing all the moving parts so a prompt change doesn't break production silently.
What security concerns are specific to agentic AI workflows, and how do you handle them in the backend?
What they are checking: Awareness of prompt injection, tool permissions and data exposure risks in systems that take actions.
How do you use AI-assisted development tools in your own work, and how do you review what they produce?
What they are checking: Productive use of AI coding tools with real review discipline for correctness and security.
How would you monitor an AI service in production beyond uptime?
What they are checking: Tracking latency, cost, error rates and output quality signals that matter for AI systems.
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Practice this out loudThe full job description
As published by Vizient.