Evaluate AI infrastructure without the hype.
Assess systems ownership and production judgment instead of relying on an inflated title.
AI infrastructure is used to describe very different work: model serving, data systems, evaluation infrastructure, orchestration, observability, developer platforms, and the reliability layer around all of them.
Ask what they operated
Move from tools and titles to a specific production system. What depended on it? What failed? What did the candidate own when it failed? Which constraint shaped the design?
Follow the tradeoffs
- Reliability against iteration speed
- Latency against quality or cost
- Shared infrastructure against team-specific needs
- Build against buy
- Operational simplicity against flexibility
The goal is not a preferred answer. It is evidence that the candidate can identify the tradeoff, choose deliberately, and explain what would change the choice.
Separate exposure from ownership
Working near models is different from owning the systems that make them dependable. Ask where the candidate set direction, where they contributed, and where another team held the production responsibility.
Match depth to the company stage
An early team may need someone who can establish the first durable system while still shipping product. A larger environment may need deeper specialization. Neither profile is universally stronger.
Evaluate the system they owned and the decisions they made. Let the title come last.