• Design, build, and maintain scalable, resilient data and ML pipelines, infrastructure, and workflows using tools such as GitHub Actions, ArgoCD, Crossplane, Terraform, Helm, and others. • Automate infrastructure provisioning and configuration management using cloud-native services (preferably AWS) with tools like Terraform, CloudFormation, or Crossplane. • Design, containerize, and manage Kubernetes (EKS) clusters and/or ECS environments in AWS. • Collaborate with development teams to optimize performance, deployment, and cost. • Partner with DevOps and SRE teams to ensure high availability, observability, scalability, and security of the data and ML infrastructure. • Work closely with Data Scientists and ML Engineers to operationalize machine learning models, including building CI/CD pipelines for model training, validation, and deployment. • Implement observability for data pipelines and ML services using tools like Prometheus, Grafana, Datadog, or similar. • Develop and maintain automated pipelines for model retraining, monitoring drift, and versioning in production. • Support experimentation and prototyping in areas such as Machine Learning and Generative AI, transitioning successful prototypes into production systems. • Ensure cloud infrastructure is secure, compliant, and cost-efficient, following best practices in governance, identity, and access management.