Sai Ram

Architected cost-efficient AI platform infra and modernized cloud pipelines for high-scale workloads; Led multi-cloud strategies, optimized GPU usage via spot instances, and implemented auto-scaling, monitoring, and security controls to reduce costs and improve reliability.

Remote consulting on high-scale infrastructure and AI platform modernization. Key project includes AI cost optimization: architecting a cost-efficient inference platform using vLVM and Karpenter on AWS EKS; replacing on-demand GPU instances with a spot orchestration strategy to reduce inference costs by 40%. Implemented deep observability with eBPF/Cilium to visualize network bottlenecks and GPU idle time, enabling precise ML throughput tuning. Built an enterprise ML Ops platform with Kubeflow and ArgoCD, automating the model lifecycle from commit to deployment and reducing ship time from days to hours. Designed a multi-cloud FinOps strategy connecting AWS and OCI via FastConnect, routing batch workloads to cheaper OCI compute while keeping front-end services on AWS; reduced data egress and compute costs by 30%.

Remote DevOps/Cloud consulting focusing on AI platform modernization, high-scale Kubernetes workloads, and cost optimization. Architected infrastructure to run on AWS EKS/GKE/AKS, introduced multi-cloud governance, and implemented spot-instance strategies to reduce costs. Implemented eBPF-based observability to pinpoint network bottlenecks and GPU idle time, enabling precise ML throughput tuning and reliability improvements.