Platform Architecture for Organizational Scale Questions

Designing internal platforms and infrastructure to support large engineering organizations and evolving teams. Topics include developer experience and self service platform design, deployment platforms that enable safe frequent releases for hundreds of engineers, platform automation and observability patterns that provide cross service visibility, governance and operational policies, service onboarding and lifecycle, and how to evolve platform capabilities as headcount and service count grows. Candidates should discuss trade offs between centralized platform services and team autonomy, metrics for platform health, and approaches to encourage adoption while minimizing operational friction.

HardSystem Design

0 practiced

Design a platform-wide observability pipeline for AI workloads that ingests logs, metrics, traces, model-specific metrics (prediction distributions, drift), and feature lineage. It must support fast querying, long-term retention tiering, cost controls, and alerting. Propose ingestion, processing, storage tiers, retention/rollup policies, and sampling/aggregation strategies.

EasyTechnical

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Design a simple rollout plan using feature flags for a model update: start at 1% of users, monitor key metrics for regression, and progressively ramp to 100% over time. Specify the metrics to track, rollback criteria (quantitative), and how to automate the ramp and rollback process.

MediumTechnical

0 practiced

Your org runs many GPU training jobs with low utilization and high cost. Propose a set of technical and process changes to reduce cloud spend while preserving developer productivity. Cover scheduling policies, instance types, use of preemptible/spot instances, pooling, autoscaling, and incentives or chargeback models.

EasyTechnical

0 practiced

Using kubectl, write a single bash command that lists all running pods labeled app=model-server across all namespaces and shows pod name, namespace, node name, and age. Explain any flags or jsonpath/custom-columns you used and how to filter for only Ready pods.

MediumTechnical

0 practiced

Case study: a newly deployed model begins returning biased outputs detected by customers. As the platform engineer, outline an incident response runbook: detection signals, containment and mitigation steps to reduce customer impact, rollback/serve-previous model strategy, root cause analysis steps, and platform-level preventive controls you would add.

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