Deep Technical Expertise and Project Mastery Questions

In depth exploration of the candidate's most complex technical work and domain expertise. Interviewers will probe architectural decisions, design trade offs, performance and reliability considerations, algorithmic or model choices, and the reasoning behind technology selections. Candidates should be ready to walk through a single complex backend or artificial intelligence and machine learning system in detail, explain low level technical choices, discuss alternatives considered, describe challenges overcome, and justify outcomes. Expect follow up questions that test depth of understanding and the ability to defend decisions under scrutiny.

HardSystem Design

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Design a multi-tenant inference architecture where tenants require strict data isolation and per-tenant rate-limits, but the platform should reuse model binaries when possible. Discuss tenancy isolation approaches, encryption boundaries, scheduling, and billing attribution.

HardSystem Design

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Design a global feature caching layer to provide low-latency feature reads for inference across regions. Describe cache topology, eviction strategy, invalidation protocols, and how you would bound staleness to meet model accuracy targets while minimizing cross-region traffic.

MediumTechnical

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You need to implement request batching in a model server to increase GPU throughput but must keep tail latency under a 100ms SLO. Describe the batching algorithm, how to control maximum batch size and waiting time, and how you would evaluate its effectiveness in production.

HardSystem Design

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Design a secure model-serving architecture that processes PII-sensitive inputs across regions while enabling observability and debugging. Explain encryption at rest and in transit, key management, access controls, and how to reconcile security with the need for traces and logs.

HardTechnical

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Third-party feature APIs your model relies on have variable latency and occasional timeouts. Design resiliency patterns for your inference path to handle such external dependencies gracefully while minimizing accuracy loss. Include caching, fallback predictions, and degradation strategies.

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