Developed an end-to-end test for the llama_3_1_405b_nemo model targeting a3ultra hardware within the GoogleCloudPlatform/ml-auto-solutions repository, focusing on robust hardware validation and streamlined release cycles. The work introduced a DAG-based workflow to automate test execution, updated CODEOWNERS to clarify responsibilities, and refined Docker image versioning to ensure model-specific deployments. Leveraging Python, CI/CD practices, and cloud engineering skills, the solution reduced misconfigurations and deployment delays for hardware-accelerated inference. No critical bugs were addressed during this period, but the feature enhanced feedback loops and contributed to safer, more reliable releases in a cloud-based MLOps environment.