Data Engineering for AI

Most AI projects never make it to production. This skill is what keeps them alive. The work is designing, building, and maintaining the data infrastructure that AI systems actually run on: ETL pipelines for training data, real-time streaming for inference, vector database management, embedding pipeline optimization, data versioning, and quality monitoring that catches issues before they reach a model. Then there's the hard stuff that didn't exist five years ago, namely unstructured data processing, multi-modal pipelines, and the freshness guarantees that RAG and fine-tuning workflows quietly depend on.