Role library · aec-roles-v1 · technology
AI Exposure Check
Cloud Engineer
Illustrative task-level Exposure from the offline sample model — not a market survey.
Exposure 57% average exposure · CHF 109440 illustrative time value · 5 tasks
Tasks
Exposure and reasoning
| Task | Exposure % | Reasoning |
|---|---|---|
| Write boilerplate CRUD endpoints | 80% | High generative AI exposure: “Write boilerplate CRUD endpoints” is largely pattern-matching, drafting, or structured data work that models can assist with human review. |
| Generate unit-test scaffolds | 75% | High generative AI exposure: “Generate unit-test scaffolds” is largely pattern-matching, drafting, or structured data work that models can assist with human review. |
| Summarise incident postmortems | 68% | Moderate exposure: “Summarise incident postmortems” mixes templateable steps with contextual judgment; AI can draft or triage, but an accountable human should decide. |
| Review pull-request diffs | 46% | Lower-moderate exposure: “Review pull-request diffs” requires coordination or domain nuance; AI may summarise inputs but should not own the outcome. |
| Pair on novel architecture | 16% | Low exposure: “Pair on novel architecture” is relationship-, safety-, or judgment-critical; keep a human owner and use AI only as a narrow aid. |
Sample actions
- Map data-classification rules before connecting internal systems to models.
- Rewrite SOPs so AI-assisted steps keep an accountable human owner.
- Train the team on prompt libraries tied to existing templates.
What these figures are not
- Illustrative role library from the offline AEC sample model (aec-ai-v1), not a surveyed occupation census.
- Task packs and exposure scores are deterministic samples for self-serve triage — Faethm-style ontology breadth, not Pearson proprietary data.
- CHF time-value estimates use illustrative Swiss mid-market rates and annual hours.