Over a two-month period, contributed to the vllm-project/production-stack and aws/sagemaker-hyperpod-cli repositories, focusing on deployment reliability and production-ready machine learning workflows. Addressed a critical Helm upgrade issue by correcting the install script path for kube-prom-stack.yaml, which improved deployment stability and traceability in Kubernetes environments using Shell scripting. Later, developed an end-to-end HyperPod CLI workflow for AWS SageMaker, enabling streamlined cluster setup, training, inference deployment, and governance controls. Leveraged Python, AWS, and SageMaker to deliver comprehensive documentation and examples, reducing onboarding time and supporting operational readiness for machine learning workloads in production environments with clear, reproducible processes.