We have presented a practical implementation of a ML-based electrostatic embedding model to carry out molecular dynamics simulations with hybrid ML/MM schemes. The developed emle-engine package is based on the EMLE embedding scheme, which predicts the ML-MM interaction energy using physics-based models. The static component of the electrostatic interaction energy is calculated by approximating the in vacuo electron density of the embedded subsystem using MBIS partitioning. The induction component is obtained from the Thole model. This functional form incorporates the essential physics of the QM/MM interaction energy with electrostatic embedding and therefore makes the model generic, despite relying on only a handful of free parameters.