Mixture models are difficult to inference - you need to carefully construct the prior and put constraint to limit mode switching during sampling. Unfortunately without the data it is difficult to give you more insight.
If you already have the mixed-effect model set up, you can do clustering on the random effect slope - after all it is valid to compute expectation of some function using the MCMC samples. I would try Kmean clustering on each iteration and then compute the mean.