@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
:param training_args: A list of command-line arguments to pass to the
        training program. (templated)
    :type training_args: List[str]
    :param scale_tier: Resource tier for MLEngine training job. (templated)
    :type scale_tier: str
    :param master_type: The type of virtual machine to use for the master
        worker. It must be set whenever scale_tier is CUSTOM. (templated)
    :type master_type: str
    :param master_config: The configuration for the master worker. If this is
        provided, master_type must be set as well. If a custom image is
        specified, this is mutually exclusive with package_uris and
        training_python_module. (templated)
    :type master_config: dict
    :param runtime_version: The Google Cloud ML runtime version to use for
        training. (templated)
    :type runtime_version: str
    :param python_version: The version of Python used in training. (templated)
    :type python_version: str
    :param job_dir: A Google Cloud Storage path in which to store training
        outputs and other data needed for training. (templated)
    :type job_dir: str
    :param service_account: Optional service account to use when running the training application.
        (templated)
        The specified service account must have the `iam.serviceAccounts.actAs` role. The
        Google-managed Cloud ML Engine service account must have the `iam.serviceAccountAdmin` role
        for the specified service account.
        If set to None or missing, the Google-managed Cloud ML Engine service account will be used.
    :type service_account: str
    :param project_id: The Google Cloud project name within which MLEngine training job should run.
        If set to None or missing, the default project_id from the Google Cloud connection is used.