Worked on the Azure/telescope repository to deliver a scalable benchmarking feature focused on large-scale image pull testing in Kubernetes environments. Developed and validated a 1000-node ACR image pull benchmark, updating pipelines and topology to support concurrent pulls of 5–10GB images. Introduced a memory_request_override in the CRI module using Python and YAML, addressing pod scheduling stability when max_pods is low. Enhanced test parametrization to enable flexible matrix-scale runs and improved CI observability for reproducible results. Leveraged skills in Kubernetes, performance testing, and Terraform to ensure robust validation and documentation of large-scale workflows, emphasizing reliability and scalability in cloud-native testing.