In the energy industry, high performance computing has long been a significant enabler for solving compute challenges. Seismic imaging, reservoir engineering, computational chemistry, CFD simulations, and many other large-scale challenges in the modelling and simulation space necessitate HPC resources to solve and expedite workflows. However, large scale data processing using Deep Learning (DL) techniques is yet to be exploited to its full potential by energy industry. There has recently been enough interest in the geophysics community to leverage the power of DL to make seismic processing and imaging workflows more efficient. In this tutorial, we first present a high level overview of the challenges and opportunities in seismic processing and imaging workflow, and propose an in-depth analysis of the performance engineering aspects of DL-based workflows.