This. We need to start treating ML as a collection of fields with synergies. Just like they teach in engineering school, you're going for a T shaped skill tree. Be generally knowledgable about what's out there, but do a deep dive of one particular niche that interests you.

Also, it should be said that if you're not a researcher, one of the hardest parts of AI is the data. Everyone gets this wrong, particularly when they start chasing the latest backbone, or loss function, or a hundred things that see dozens of publications per week on. The ML engineering around standing up a good experiment, regardless of what you're actually putting into the model slot of this experiment, is where 90% of the effort should be going.