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MLOps & ML Engineering / 31
hardCapital OneJPMorganAmazon

Design an end-to-end MLOps platform on AWS for a regulated lender under SR 11-7.

A staff-level platform design where the constraint set, not the architecture, is the test. The controls SR 11-7 actually demands, how to make audit a byproduct of the pipeline instead of a quarterly fire drill, and the v1 you can ship without a compliance finding.

Updated Sep 2026 · Grounded in real Forward Deployed Engineer interview loops and written to a senior-engineer editorial bar.

A staff-level platform design where the constraint set, not the architecture, is the test. The controls SR 11-7 actually demands, how to make audit a byproduct of the pipeline instead of a quarterly fire drill, and the v1 you can ship without a compliance finding.

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FEDITOR'S NOTE

The reflex that fails this is bolting governance on at the end as an 'audit dashboard'; the senior move is making every control a gate the pipeline cannot skip, so evidence is produced by construction. The held-back follow-up is almost always 'show me how a declined application traces back to its training data', and if your design can't answer it in one query path you've described a research platform, not a regulated one. Have a clear position on segregation of duties: the person who trains a model must not be the identity that promotes it, and that's an IAM boundary, not a policy memo.

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