In recent years, conventional Artificial Neural Networks (ANNs) have become essential in research and industry, serving as the primary solution for a wide range of applications. Simultaneously, there is growing interest in Spiking Neural Networks (SNNs), which offer a more biologically plausible model by emulating the human brain's structure and function. SNNs excel in sparse and parallel processing, associative memory and low power consumption. To fully leverage these advantages, specialized neuromorphic hardware is required, shifting from traditional von Neumann architectures to event-driven, asynchronous computation. This thesis presents a modular approach for designing SNNs suitable for deployment on Intel's Loihi 2 neuromorphic hardware through Intel's own framework, Lava.