Deciding tools to use for ML
Well, I think your question does not have a "correct" answer since you are looking for a piece of advice on ML model deployment. 

As you mentioned, you have a complete back-end in java, and you should not drop it. 
Instead, (1) you can take advantage of a micro-services oriented approach. With micro-services, you can communicate different pieces of code using HTTP. You may have some of your back-end controllers in java, and some kind of ML-managers to wrap prediction routines with web routes.

I think it's a good idea for you to learn python, and also python web frameworks (flask is a very simple one, you can google "ML deployment flask" or something like that, and there is plenty of tutorials). Also, (2) you can convince your data scientists to write their micro-services for you because it is simple to do. Then, you can get their code and adapt to better look like a web-service.

Another way to take advantage of your java back-end is to (3) try to translate the python/R ML code to Spark MLlib, which has many powerful methods for you to write your java code. However, you will eventually have to code some operations that your data scientists made in python/R, which is not available in MLlib. 

It depends, but I hope that it helps!