AI Data Engineering

Purpose

Build data infrastructure for AI/ML systems including RAG pipelines, feature stores, and embedding generation. Provides architecture patterns, orchestration workflows, and evaluation metrics for production AI applications.

When to Use

Use this skill when:

• Building RAG (Retrieval-Augmented Generation) pipelines
• Implementing semantic search or vector databases
• Setting up ML feature stores for real-time serving
• Creating embedding generation pipelines
• Evaluating RAG quality with RAGAS metrics
• Orchestrating data workflows for AI systems
• Integrating with frontend skills (ai-chat, search-filter)