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Role guide
AI / Data Platform Engineer
- Design evidence-grounded data and AI platform changes. - Review reliability, security, lineage, performance, and cost constraints. - Prepare implementation and validation plans.
Read the fileOverview
AI / Data Platform Engineer Overview
ONET describes database architects as designing enterprise database strategies, data models, integrations, security measures, performance, and scalable data systems.
Read the fileWorkflow
AI / Data Platform Engineer source-backed Triage
1. State the requested decision or deliverable. 2. Inventory evidence: workload and SLO requirements, source schemas and contracts, data classification, current architecture, identity and.
Read the fileTemplate
AI and data platform implementation brief
State the direct answer, recommendation, or draft purpose.
Read the fileIs this bundle right for your task?
Who it is for
- People performing or supporting AI / Data Platform Engineer work, plus teams reviewing its decisions and outputs
- Teams working in Data and analytics, Software
When to use it
- An AI / Data Platform Engineer task needs a structured plan, evidence checklist, or review-ready output.
- A recommendation needs its assumptions, owners, risks, dependencies, and success measures made explicit.
What you need to provide
- The task objective, intended audience, working context, constraints, source material, and decision owner.
- Relevant reports, exports, examples, policies, prior decisions, and success measures available for the task.
Tasks and expected outputs
Questions it helps answer
- Plan platform changes without fabricating infrastructure.
- Surface lineage, access, reliability, and cost dependencies.
- Produce review-ready implementation plans with rollback.
What it helps produce
- AI and data platform implementation brief
Practical example
Use it with an agent
Load the bundle as context, provide the evidence named above, then adapt this example to your situation.
Provide the task objective, intended audience, working context, constraints, source material, and decision owner. Ask the agent to approach AI / Data Platform Engineer work by producing AI and data platform implementation brief with a prioritized plan, evidence checks, owners, risks, and unresolved questions. Begin with O*NET OnLine — Summary / 15 1243.00, then confirm that the reference is current and applicable. Inspect AI / Data Platform Engineer before drafting.
Context path: bundles/roles/ai-data-platform-engineer
What the bundle includes
Frameworks
- source-evidence matrix
- platform-evidence matrix
- qualified-review gate
Evaluations
- AI / Data Platform Engineer source verification check
Sources used to build this bundle
These are the public references behind the role definition and operating guidance. The bundle does not replace current documentation or evidence from your site.
Limitations and safe use
Do not use this for
- Treating the bundle as a substitute for organization-specific authority, firsthand evidence, or accountable review.
Known limitations
- Environment-specific work requires current schemas, architecture, workload, policy, access, and telemetry evidence.
- This bundle does not certify model safety, privacy, or compliance.
- Do not infer resource names, capacity, cost, deployment state, or data sensitivity.
Safety notes
- Do not expose credentials, secrets, personal data, or proprietary datasets.
- Require confirmation before infrastructure, access, retention, model, or production changes.
- Route security, privacy, model-risk, and compliance decisions to accountable reviewers.