• Own pipelines and DAGs that move data, features, embeddings, and models through the ML lifecycle • Design/maintain ranking and retrieval services that run models in real-time • Build scalable model-serving APIs, ensuring reliability, efficiency, and performance • Create reusable infrastructure that other MLEs depend on to train, deploy, and iterate on models • Ensure pipelines and systems support high scale, low latency, and operational excellence • Enable modeling with better systems, features, and deployment pathways