How would you take an AI feature from a product idea to a production release?I would first align with product and data science on the user problem, success metrics, data constraints, and acceptable risks. I would design a thin vertical slice that validates the highest-risk assumptions, then define service boundaries, API contracts, observability, security controls, and rollout criteria. After validating performance and reliability, I would use feature flags and monitoring to release incrementally and iterate from production feedback.
How do you balance rapid experimentation with production engineering standards?I separate exploratory work from production paths while ensuring experiments have clear hypotheses, measurable outcomes, and defined exit criteria. I prioritize lightweight reusable components, automated tests for critical paths, logging, cost controls, and documented interfaces. This allows the team to move quickly without allowing prototypes to become unmaintainable production dependencies.
What practices would you use to operationalize an ML or LLM-based service?I would implement versioned models or prompts, reproducible deployment pipelines, input and output validation, latency and error monitoring, and quality evaluation datasets. I would also track model drift or behavioral regressions, establish fallback behavior, and monitor cost and safety signals. For LLM use cases, I would include prompt governance, guardrails, and human-review paths where risk warrants them.
Describe how you would collaborate with a data science leader and product manager on feasibility tradeoffs.I would translate product goals into technical options with explicit tradeoffs across accuracy, latency, cost, delivery time, and operational complexity. With the data science leader, I would clarify model assumptions, data readiness, and evaluation metrics; with product, I would identify the smallest valuable release. I would document decisions and recommend a path that meets business needs while leaving room for iteration.
How do you ensure an AI service remains reliable after launch?I define service-level indicators for availability, latency, error rates, quality, and cost before launch. I add structured logging, tracing, dashboards, alerts, rate limits, fallback responses, and incident runbooks. Post-launch, I review usage and outcome data regularly, run controlled experiments, and prioritize improvements based on user impact and operational evidence.
