Technical Leadership and Architectural Influence Questions

Demonstrating leadership in technical decisions at the architecture or system level. Candidates should prepare concrete examples where they identified architectural problems, evaluated alternative solutions and trade offs, proposed a preferred design, gained buy in from engineers and stakeholders, and drove implementation. Discuss systems thinking and long term impact on team velocity, code quality, reliability, and product features. Include examples of championing new tools or frameworks, leading migrations or refactors, negotiating trade offs between time to market and technical debt, and occasions when you reversed a decision based on new data. Emphasize communication of complex technical ideas, consensus building with peers, and measurable outcomes.

HardSystem Design

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You're asked to migrate long-running generative requests from synchronous request/response to asynchronous streaming (progressive token delivery) without breaking SLAs for short, latency-sensitive requests. Design the architecture: client contract, correlation ids, streaming protocols, backpressure, cancellation, and how you would run a staged migration.

EasyTechnical

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How would you convince engineering peers and business stakeholders to accept a necessary refactor of an AI service that increases short-term cost but reduces long-term risk? Describe the communication approach, artifacts (cost/benefit, prototypes), and decision criteria you would present.

HardTechnical

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Design an online learning architecture that accepts incremental updates from streaming user interactions. Explain how you would ensure model convergence, preserve reproducibility of training, manage conflicts across replicas, safely validate incremental updates before promotion to production, and support rollback to previous checkpoints.

EasyTechnical

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Explain what a canary rollout is and outline a step-by-step canary plan for deploying a new NLP model to production. Include initial traffic percentage, monitoring windows, success metrics (latency, error rates, quality metrics), rollback criteria, and the ramp-up strategy to reach full rollout.

MediumTechnical

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You're asked to reduce p99 latency by 2x across a chain of microservices serving embeddings. Provide a profiling-driven plan that covers instrumentation, identifying hot paths, code-level optimizations, RPC/serialization choices, caching, backpressure, and a risk-managed rollout strategy for each optimization.

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