Worked on the GoogleCloudPlatform/ml-auto-solutions repository to enhance the reliability of MaxText checkpointing by implementing comprehensive end-to-end test coverage. Developed tests in Python that validate both synchronous and asynchronous checkpointing modes, updating the DAG to iterate through each mode and ensuring the correct test flags are applied. This approach improved the robustness of checkpoint validation and enabled more thorough regression testing, reducing production risk and supporting safer deployments. Leveraged skills in cloud infrastructure, MLOps, and testing to update the test infrastructure, making it mode-agnostic and better suited for ongoing validation of checkpointing functionality in production environments.