What’s dying is classical feature engineering: complex ad-hoc pipelines created to manipulate and transform data to improve a specific model.

Contemporary machine learning instead prefers to create complex model architectures and loss functions which imbue certain inductive biases that guide a model toward learning useful transformations.

In this way, modern machine learning is just “meta” feature engineering. It’s one level abstracted, but still feature engineering under the hood.

Of course feature engineering will always be around, especially in the abstract sense of “transforming data”, which the OP alludes to with examples like transforming CCD sensor readings to RGB pixel space.