Design, build, and operate scalable data and AI platform capabilities across cloud-native and hybrid environments, with a primary focus on Azure and exposure to AWS, ensuring reliability, security, and operational excellence.
Build and maintain foundational platform services for data and analytics using Azure Databricks, ADLS Gen2, and supporting cloud services.
Develop and manage Infrastructure-as-Code (IaC) and containerized platform components using Terraform, Docker, and Kubernetes fundamentals for repeatable, resilient deployments.
Enable standardized, self-service platform patterns for data engineering, analytics, and AI teams.
Integrate data platforms with AI platforms and model AI gateways, supporting secure, governed, and scalable AI access.
Apply platform engineering and DevOps best practices through automation, CI/CD, and DataOps workflows using Python and scripting.
Collaborate with platform, AI/ML, application, infrastructure, and security teams to deploy, scale, and operate platform workloads.
Monitor, troubleshoot, and support production platforms in distributed, cloud-native environments, meeting reliability and SLA expectations.
Contribute to platform standards, reusable patterns, documentation, and runbooks to improve self-service and developer productivity.