I am a co-founder of deep learning startup -
https://tensorflight.com. Engineers without PhD, but some deep learning experience, are useful on applied deep learning teams like ours. Please get in touch at kozikow@tensorflight.com.
Training model itself takes maybe 10-20% of overall engineering effort. You need to have at least one person on the team, who grinded their teeth on training deep learning models. My co-founder says at least half a year of full time experience. I would say that there is nothing that would fundamentally require the PhD, just that very few people without the PhD have the sufficient experience right now. What's the most important is an intuition what method will help the most in your situation.
Within model training there are many tasks that can be off-loaded to someone without heavy deep learning expertise - e.g. data augmentation or evaluation statistics. Someone with deep learning experience is still required to know which tasks will have the highest impact.
Except the model training there is lots of work on everything around it. Inference pipeline, web server, data gathering, hardware. Majority is "plain engineering", but there is a few engineering skills specific to deep learning - e.g. inference pipeline or hardware to train models. What's more, even within the domain like computer vision, "classic" methods are sometimes still more cost effective than deep learning.