When does a company actually decide to hire an ML engineer instead of just using APIs? [D]

I’m trying to understand this from a real-world perspective.

Right now, it feels like you can get very far just using existing models (LLMs, embeddings, etc.) through APIs. You can build solid products without ever training a model yourself.

So my question is:

At what point does a company actually need to hire an ML engineer?

Not in theory, but in practice.

Some situations I’m thinking about:

• Is it when API costs get too high at scale?
• When they need better performance on their own data?
• When the product depends heavily on predictions (forecasting, ranking, etc.)?
• When they need more control, reliability, or evaluation?

Also curious about transitions like:

• “We started just calling APIs, but then we had to hire ML engineers because ___”
• Cases where ML engineers made a real difference vs cases where it wasn’t necessary

Basically trying to understand:

Where is the line between:
→ “just use existing models”
and
→ “you need someone who actually builds/owns ML systems”

Would appreciate any concrete examples or experiences.