Describe a production LLM feature you delivered and the tradeoffs you made around latency, reliability, cost, and user experience.I would explain the feature architecture, including provider abstraction, prompt and context management, streaming behavior, fallbacks, telemetry, and evaluation criteria. I would quantify key outcomes such as response latency, error rates, token cost, and adoption, then describe how I iterated based on production data.
How would you design a streamed AI interview-assessment experience that supports tool or function calling?I would separate orchestration from the client stream, use typed tool contracts and server-side authorization, persist request and tool-call state, and expose incremental events through a resilient streaming protocol. I would include cancellation, retry and idempotency rules, provider fallback behavior, audit logs, and clear UI states for partial or failed responses.
Tell us about a time you reduced technical debt without stopping feature delivery.I would identify the operational or delivery pain caused by the debt, define a small sequence of safe improvements, and prioritize them alongside feature work. I would use tests, instrumentation, migration plans, and measurable checkpoints so the team could improve maintainability while continuing to ship.
What is your approach to reviewing AI-generated code produced by coding agents?I treat AI-generated changes as untrusted draft code and verify requirements, security boundaries, correctness, tests, dependencies, and operational impact before approval. I scope tasks narrowly, provide relevant context, inspect diffs carefully, and require the same quality bar, ownership, and documentation as for human-authored code.
Describe how you respond to a production incident and ensure the team learns from it.I first focus on user impact, mitigation, and clear stakeholder communication while using dashboards, logs, and recent-change history to guide diagnosis. After recovery, I document the timeline and root causes, then drive concrete follow-ups such as alerts, runbook improvements, tests, safeguards, and ownership dates to prevent recurrence.
