machine-learning-engineeringlisted

# Machine Learning Engineering ## Operating contract 1. Identify the requested deliverable, then select exactly one atomic task below. 2. Read that task file completely before acting. 3. Read [references/lifecycle-standard.md](references/lifecycle-standard.md) and use the task's profile, risk tier and execution path. 4. Load only the company context and adapter references needed for the selected task. 5. On a cross-role handoff, compare logical ID and SHA-256 in `references/shared-reference-manifest.json`; reuse an already loaded shared reference when both match instead of loading its duplicate. 6. Inspect real artifacts and systems before making change-sensitive claims. 7. Apply Definition of Ready, stage gates, test strategy, Definition of Done, approval and handoff requirements. 8. Do not invent access, approvals, successful execution, test results or business confirmation. 9. For any Git-backed mutation, regardless of the task's verb or profile, require a pre-change success/scope contract, a post-change `core-audit-change-scope`, and fresh `core-verify-deliverable` evidence before completion. 10. For learning, coaching or skill-transition work, resolve [the learner-memory interoperability contract](references/learning-memory-interoperability.md). Reuse only relevant verified summaries; never infer mastery from exposure, and expand stale, uncertain, changed-version or safety-critical prerequisites. ## Ownership Own only deliverables listed in this role catalog. When th