Design, deploy and maintain scalable cloud and Kubernetes infrastructure for ML workloads, including container orchestration, autoscaling and production deployments
Build and maintain RESTful/gRPC APIs and messaging services to support ML inference, data processing and real-time or asynchronous workloads
Develop reliable ML deployment and MLOps pipelines, including Docker/Podman, CI/CD, model versioning, monitoring, logging and automated deployment/retraining workflows...