Understanding of MLOps and LLMOps capabilities, including experiment tracking, model registry, feature stores, pipeline orchestration, automated quality gates, and versioning of data, models, and prompts using tools such as MLflow and Kubeflow. Integrate data, ML, and GenAI reference architectures covering data sources, feature pipelines, models or LLMs, vector stores, serving layers, consuming applications, and enterprise integration points.