I had highlighted the way to deploy custom R models in our last session in Stockholm. You need to create an "R function" endpoint which reads the .RData file in a folder and applies the script. Do you have the API package I used last time? You can use it as a starting base and adapt it to your needs.
Note that there is also a "Custom prediction (R)" endpoint which works similarly to the "R function" endpoint, but adds the ability to enrich the input JSON with external SQL tables. In your case, it should not be needed as JSON files have been enriched beforehand.
There is a visual way to attach models provided you used the visual machine learning interface to develop them. In this case you just need to create a "prediction model" endpoint and select the deployed models you want to use.