@darshan-majithiya had opened #15429 to add the hyperparameter tuning PR but it's gone stale. I'm adding this issue to see if they want to pick it back up, or if not, if someone wants to pick up where they left off in the spirit of open source 😄 
airflow/providers/google/cloud/operators/mlengine.py
model will be used.
        Should not be None if version_name is provided.
        Should be None if uri is provided. (templated)
    :type model_name: str
    :param version_name: The Google Cloud ML Engine model version to use for
        prediction.
        Should be None if uri is provided. (templated)
    :type version_name: str
    :param uri: The GCS path of the saved model to use for prediction.
        Should be None if model_name is provided.
        It should be a GCS path pointing to a tensorflow SavedModel. (templated)
    :type uri: str
    :param max_worker_count: The maximum number of workers to be used
        for parallel processing. Defaults to 10 if not specified. Should be a
        string representing the worker count ("10" instead of 10, "50" instead
        of 50, etc.)
    :type max_worker_count: str
    :param runtime_version: The Google Cloud ML Engine runtime version to use
        for batch prediction.
    :type runtime_version: str
    :param signature_name: The name of the signature defined in the SavedModel
        to use for this job.
    :type signature_name: str
    :param project_id: The Google Cloud project name where the prediction job is submitted.
        If set to None or missing, the default project_id from the Google Cloud connection is used.
        (templated)
    :type project_id: str
    :param gcp_conn_id: The connection ID used for connection to Google
        Cloud Platform.
    :type gcp_conn_id: str