Despite the high predictive power of Deep Neural Networks in complex domains, they lack interpretability. Genetic Programming (GP) can achieve intrinsic interpretability through a carefully chosen syntax. In Grammar-Guided GP (GGGP) a grammar is used to restrict the search space, helping to reduce the main downside of GP — long training times. We implement this idea in Genetic Engine, a GGGP framework that we compare with the state-of-the-art GGGP framework PonyGE2. Our results show that Genetic Engine performs on par with PonyGE2, can express more complex solutions, and is more accessible for data scientists.