  We describe a simple and efficient Python code to perform Bayesian
forecasting for gravitational waves (GW) produced by
Extreme-Mass-Ratio-Inspiral systems (EMRIs). The code runs on GPUs for an
efficient parallelised computation of thousands of waveforms and sampling of
the posterior through a Markov-Chain-Monte-Carlo (MCMC) algorithm. EMRI_MC
generates EMRI waveforms based on the so--called kludge scheme, and propagates
it to the observer accounting for cosmological effects in the observed waveform
due to modified gravity/dark energy. Extending the code to more accurate
schemes for the generation of the waveform is straightforward. Despite the
known limitations of the kludge formalism, we believe that the code can provide
a helpful resource for the community working on forecasts for interferometry
missions in the milli-Hz scale, predominantly, the satellite-mission LISA.
