I've also been building a learning to rank plugin for elasticsearch, we might want to sync up.
I hadn't set it up to sync to github as it was just internal development, but i've started the sync and it will show up at https://github.com/wikimedia/search-ltr soon.
It's got a bit more of the integration with elasticsearch put together, including storing models in cluster state and a rest interface for managing them. It's a bit more of a direct port of the solr plugin rather than a rewrite from the ground up so there are also some oddities that don't yet make sense. Refactors will certainly be done. It's also tied a little less directly to RankLib, such that i can convert and load in MART models trained by lightgbm or xgboost which have done pretty well in my offline tests and are able to utilize resources on my training machine much more efficiently than ranklib's LambdaMART (although in terms of results, the ranklib implementation is pretty good).
We store models as a custom scripting language which takes care of distributing the model around the cluster, caching and basic CRUD operations. This was the hard thing to figure out, at first we looked at a REST plugin but it seemed cumbersome and hard to integrate with the query DSL. But I'm curious how you guys got around those pain points:)
https://github.com/o19s/elasticsearch-learning-to-rank