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Engineering

AI Engineering

engineering/skills/ai-engineering

AI and data engineering skill for ML systems, pipelines, prompt workflows, evaluation, and production AI architecture.

Use for ML, data, and AI engineering work: data pipelines, ETL and ELT, DataOps, warehouses and lakehouses, streaming systems, prompt and agent design, RAG, constrained generation, experiment design, model evaluation, feature engineering, and computer vision systems from dataset prep through production deployment.

What it does

AI and data engineering skill for ML systems, pipelines, prompt workflows, evaluation, and production AI architecture.

How it works

  • Load only the reference files relevant to the current task (see Workflow Router below).
  • For data engineering: choose the processing model first — batch vs. streaming, Lambda vs. Kappa, warehouse vs. lakehouse.
  • For prompt/LLM work: choose the right pattern first — see Prompt Engineering Quick Reference below.
AI Engineering Skill | Alvaro Villalba