• Architect and guide the design of a scalable, secure ML platform supporting the full ML lifecycle, from data ingestion to model monitoring. • Design and implement infrastructure for model training, hyperparameter tuning, experiment tracking, and model registry. • Orchestrate ML workflows using tools such as Kubeflow, SageMaker, MLflow, or similar. • Collaborate with Data Scientists, MLOps engineers, Data Engineers, and Product Engineering to define best practices for reproducibility, governance, and CI/CD for ML. • Partner with Data Engineers to build robust data pipelines for model-ready datasets. • Optimize ML workload performance across compute and storage layers using cloud-native and open-source solutions. • Lead technical discussions, mentor junior engineers, and help set the technical vision for the ML platform roadmap. • Ensure compliance with security, privacy, and regulatory requirements throughout the ML lifecycle.