Unfortunately, even with large amounts of training data, hyperparameter choices can strongly influence the performance of a trained model.
What you can usually drop when you have large amounts of training data is regularisation. If your training examples cover the function space you are learning really well, then it is harder to overfit the training data. Regularisation choices are also hyperparameters, so you can save some search space and time by ignoring them.
Do we need a hyperparameter tuner if we have a sufficient number of random data for training our ANN model?
Having lots of data may mean that you can use simpler "brute force" architectures and designs, and that the end result is robust over wider range of hyperparameter choices.
You may still want to tune hyperparameters at least a little though. This tuning can be tedious to drive by manual edits and re-tries, which is where an automated tuner can help.