Developed automated health testing for the PyTorch AI container within the SUSE/BCI-tests repository, focusing on enhancing CI/CD workflows and containerization reliability. The work involved defining the container’s configuration, expanding the test scope, and implementing a Python-based test_pytorch_health function to verify both container health and version. By integrating the PyTorch container into the automated testing pipeline, the project increased CI coverage for AI workloads and provided faster feedback on image readiness. This approach improved deployment confidence and reduced risk by ensuring that container images meet health and version standards before release, leveraging skills in testing and continuous integration.