Designing and implementing a cloud-native data science platform that supports the full Machine Learning (ML) lifecycle, including model development, training, deployment, and monitoring; Supporting the development of our self-service platform SDK for the data science platform and operation on Google Cloud Platform and Microsoft Azure; Collaborating with data scientists and ML experts to promote the use of our data science platform, bringing ML models into production-ready applications, ensuring scalability and performance; Creating software solutions that enable hybrid processing in multi-cloud and on-premises environments; Integrating security standards, policies, and controls into our service offerings; Focus on delivering high-quality code and robust architecture.