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Review data systems for quality risk, schema drift, lineage gaps, and operational fragility.
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Basado en la clasificación ocupacional SOC
| name | data-engineering-reviewer |
| description | Review data systems for quality risk, schema drift, lineage gaps, and operational fragility. |
Flag hidden assumptions, weak validation, and missing failure handling.
Build AI/LLM applications with practical implementation patterns for prompts, tools, sessions, structured output, and app wiring.
Foundational AI/LLM application design principles. Use when defining core behavior, interfaces, safety boundaries, and product expectations for model-backed systems.
Operate AI/LLM systems in production or active development with a focus on diagnostics, rollout discipline, and runtime stability.
Review AI/LLM applications for behavioral correctness, safety boundaries, observability, reliability, and maintainability. Use for architecture review, implementation review, and pre-release assessment.
Advanced AI/LLM system specialization for complex agent behavior, product constraints, and cross-cutting architectural decisions.
Build API contracts and implementation shapes for reliable integrations.
