Transform the model into a TensorFlow model and execute a hyperparameter tuning job on AI Platform, despite the additional conversion overhead.

Switch the model to a Keras model and utilize Keras Tuner for hyperparameter optimization, considering the ease of use but potential limitations with PyTorch.

Execute a hyperparameter tuning job on AI Platform with custom containers, allowing for direct control over the PyTorch environment without framework conversion.

Deploy a Kubeflow Pipelines instance and conduct hyperparameter tuning using Katib, which supports PyTorch natively but requires additional infrastructure setup.

Utilize both AI Platform with custom containers for immediate needs and Kubeflow Pipelines for long-term scalability and flexibility.