• Implement production AI / ML workloads using Ray and Anyscale, such as: - Distributed model training - Scalable inference and serving - Data preprocessing and feature pipelines • Advise customers on ML system architecture, including: - Application design for distributed execution - Resource management and scaling strategies - Reliability, fault tolerance, and performance tuning • Guide customers through architectural and operational changes required to adopt Ray and Anyscale effectively • Partner with customer MLE and MLOps teams to integrate Ray into existing platforms and workflows • Support CI/CD, monitoring, retraining, and operational best practices • Help customers transition from experimentation to production-grade ML systems • Enable customer teams through working sessions, design reviews, training delivery, and hands-on guidance • Contribute feedback from the field to product, engineering, and education teams • Help develop reference architectures, examples, and best practices based on real customer use cases