ML Research in Academia
Pros
- Depth of technical understanding
- Opportunity to make a novel contribution
- Long-term more credibility and opportunities
- Research contacts
- Key scientifc skills: critical thinking, independent working, etc.
Cons
- Poor/very poor work-life balance
- Short to medium term financial sacrifice
- Academic culture is not for everyone

ML Masters + Industry Job
Pros
- Some technical understanding
- Key software skills
- Real world product delivery
- Finances
- Someone will care about what you're doing
Cons
- Projects can be a bit boring
- Just get something to work, usually cut-down approaches from academia
- Little theoretical understanding

Source: ML PhD graduate transitioned to industry

Generally I would say that if you really care about the underlying theory of ML then do a PhD, but be sure to consolidate that with coding experience in actual projects. You will only have trouble with jobs if you focus solely on the theory. Working in high demand research areas such as object detection, natural language processing etc also helps. ML in industry is all about using techniques developed in academia so there is an opportunity to understand the theory but people will not care so much about that, it will be more about debugging software, training models etc. Good luck and don't feel too old - a lot of uni academics don't have a background in DL as it is a comparatively new field and they are much older than you haha.