• Manage, scale, and optimize cloud environments used for data science workloads (primarily AWS, Databricks, dbt). • Provision, maintain, and optimize compute clusters for ML workloads (e.g., Kubernetes, ECS/EKS, Databricks, SageMaker). • Implement and maintain high-availability solutions for mission-critical analytics platforms. • Deep expertise in AWS resource management and provisioning, including IAM roles and permissions. • Develop CI/CD pipelines for model deployment, infrastructure-as-code (IaC), and automated testing using industry standard toolchains • Build monitoring, alerting, and logging systems for cloud and ML infrastructure (e.g., Datadog, CloudWatch, Prometheus, Grafana, ELK). • Automate provisioning, configuration, and deployments using tools such as Terraform and CloudFormation, GitHub actions, etc. • Enable and improve data ingestion, transformation, and model execution workflows through platform capabilities and automation. • Develop and maintain self-service capabilities for data scientists to provision and manage reliable, reproducible environments for research and development. • Collaborate with Data Engineering to maintain integrations between data pipelines and cloud systems. • Share responsibility for provisioning and operating application networking capabilities that support data platforms, including API gateways, CDNs, application load balancers, TLS, and WAFs. • Implement and operationalize data science security and compliance controls for data science platforms in alignment with enterprise cloud standards. • Conduct periodic risk assessments,best practice reviews, and remediation efforts to strengthen security and resiliency. • Support secure handling of sensitive financial data. • Partner with data scientists, machine learning engineers, and data engineers to deeply understand and support their needs and workflows within data-driven initiatives. • Serve as a technical advisor on cloud architecture, performance optimization, and production readiness for data and ML platforms. • Adopt and champion Agile, DevOps, and Platform Engineering practices (kanban, scrum, continuous improvement, automation, Everything-as-a-Service) • Demonstrate a strong, proactive focus on serving internal customers, prioritizing user experience, identifying opportunities to leverage automation and self-service to reduce toil and cognitive load for developers and researchers.