About
I design and operate cloud platforms that help engineering teams build, deploy, and scale applications reliably. With experience across AWS, Azure, Kubernetes, Infrastructure as Code, CI/CD, GitOps, observability, and security, I focus on building production-grade platforms that are secure, automated, and easy to operate. My work spans cloud infrastructure, Kubernetes platforms, DevSecOps automation, and AI infrastructure supporting modern application and machine learning workloads.
Professional Snapshot
Platform Engineering Experience
- • Kubernetes platform architecture and operations (EKS and managed Kubernetes)
- • Infrastructure as Code using Terraform and CloudFormation
- • CI/CD automation using GitHub Actions and Jenkins
- • GitOps delivery using Argo CD
- • Secure ingress, DNS, TLS, and networking architecture
- • IAM, secrets management, and least-privilege security controls
- • Observability platforms using Prometheus, Grafana, Loki, and OpenTelemetry
AI Platform Engineering & MLOps
My AI infrastructure experience focuses on how machine learning systems are deployed, secured, monitored, and operated in production environments.
- • Containerized model serving on Kubernetes
- • KServe-based inference platforms
- • AWS SageMaker deployment workflows
- • MLflow and model lifecycle management
- • Secure AI infrastructure using IAM, RBAC, and cloud-native controls
- • Observability and reliability practices for AI/ML workloads
I approach AI platforms from a platform engineering perspective, emphasizing automation, security, scalability, and operational excellence.
How I Work
- • Clear ownership and accountability
- • Automation over manual processes
- • Strong collaboration across engineering, QA, and security teams
- • Building platforms as long-term products, not short-term fixes