Q1How would you create an enterprise AI platform?show answer
In plain words: Put shared things in one place: model access, permissions, tools, tests and monitoring. Let teams build on top with templates, and measure how many use it.
Full answer · Start from the teams' use cases and constraints, then centralize the cross-cutting concerns in a control plane: model access through a gateway, identity and tenant context, a governed tool registry, policies and guardrails, evaluation suites, tracing and cost metering. Teams consume SDKs and templates in a data plane that stays scalable and isolated, without forcing one orchestration style. Roll out with two lighthouse teams, publish golden-path templates and a readiness checklist, assign ownership and on-call, and measure adoption, time-to-production, incidents and cost per task.
Q2How would you standardize model access across teams?show answer
In plain words: Give them one gateway to all models, with rules, limits and logging. Add a list of approved models. Add a clear way to request new ones.
Full answer · Provide a model gateway as the only path to providers: it holds credentials, applies quotas and tenant budgets, safety filters and logging, routes by capability with evaluated fallbacks, and keeps model names in versioned configuration. Teams use a stable internal contract. Pair it with an evaluation harness and a catalogue of approved models and use cases, and a migration process so a provider change is a configuration and eval exercise rather than a rewrite.
Q3How would you migrate from one model provider to another?show answer
In plain words: Test both providers on real examples first. Then try the new one quietly. Move over slowly, and keep the old one until the new one is stable.
Full answer · Prove it before moving: abstract the provider behind the gateway, assemble an evaluation set from real traffic, run both providers side by side on quality, safety, latency and cost, and fix prompt or format differences. Then shadow on live traffic, canary by tenant or feature with rollback triggers, keep the old path until stable, and re-validate fallbacks. Communicate dates, own the risks (behavior differences, rate limits, data terms) and decommission deliberately.
Q4How would you govern hundreds of tools?show answer
In plain words: Keep a catalogue with owner, version and risk. Review new tools, track usage and make risky tools need approval.
Full answer · Treat tools as governed platform capabilities: a registry with owner, version, schema, risk tier, approval rules, tenants and health; onboarding review (security, data, blast radius); automated checks on schemas and descriptions; usage telemetry and tool-selection error rates; deprecation policies; and execution-time authorization independent of discovery. Tier by risk so low-risk read tools onboard quickly and high-impact write tools require approval gates and audits.
Q5How do you decide whether a new agent should get production access?show answer
In plain words: Check design, tests, security, limits, monitoring and an off switch. Start with approvals for every action, then relax as results prove safe.
Full answer · Use a launch gate scored with evidence: a design review (autonomy justified, tools least-privilege, lethal-trifecta check), an evaluation baseline with regression and adversarial tests, security review and red-team results, budgets, kill switch and rollback tested, observability and an on-call owner, data and compliance review. Start in shadow or with human approval for every action, then widen autonomy based on measured approval and reversal data.
Q6Tell me about a time you pushed back on unnecessary complexity.show answer
In plain words: Tell it in order. Say the idea and your evidence. Say how you stayed respectful and what you offered instead. End with the result you measured.
Full answer · A strong answer is specific: the proposal (for example a multi-agent design for a fixed five-step process), the evidence you gathered (a single-workflow prototype, eval numbers, cost and latency comparison), how you communicated it respectfully and what you offered instead, the decision and the measured result. Show that you separated opinion from data and kept the door open to adding autonomy where the evidence justified it.
Q7How do you handle disagreement about an architecture decision?show answer
In plain words: First agree what success means. List the options. Run a small test if needed. Decide together and write the decision down.
Full answer · Clarify the real constraints and success criteria, write down each option with trade-offs and reversibility, find a cheap experiment or prototype that can settle the key uncertainty, and decide with the people accountable for the outcome. Record the decision (an ADR) with the conditions that would change it, and commit once decided. Escalate only when the stakes are high and the data can't resolve it.
Q8Describe a production failure you owned.show answer
In plain words: Describe the impact and how you stopped it. Then give the cause and the fix. Most important, say what you changed so it will not happen again.
Full answer · Pick a real one and tell it in order: impact and detection, containment (what you switched off and how fast), root cause using traces, the fix, and — most importantly — what changed afterwards: a regression test, a guardrail, an alert, a process. Own your part without blame, quantify impact and show the improvement in a metric. Interviewers value judgment under pressure and learning over a clean record.
Q9How do you balance speed and safety on an AI product?show answer
In plain words: Be careful with risky changes and fast with safe ones. Automated tests and an easy way back let you move fast without fear.
Full answer · Tier actions by risk and match controls to tier: move fast on reversible, low-impact features with sampling review, and apply approval gates, tests and staged rollouts to high-impact ones. Automate the checks (eval gates, red-team suites) so safety doesn't depend on heroics, and use kill switches and canaries so mistakes are cheap to reverse. Safety that's built into the pipeline is what makes speed sustainable.
