The successful candidate will lead technical design and engineering decisions across the ML platform lifecycle, including governed data and feature access, model development environments, training and validation workflows, model delivery pipelines, real-time and batch inference, observability, reliability, and operational readiness. Design, build, and operate reusable platform services supporting the end-to-end ML lifecycle: governed data and features, model development, training, validation, deployment, inference, monitoring, and operations.