Key Skills for Deep Learning (AI) Engineers and how they Differ from Software Engineers — A ‘Deep’ Dive — Pun Intended

Deep Learning Engineers develop applications and systems using AI and machine learning techniques. They focus on creating and managing the AI development process, conducting statistical analysis, automating important infrastructure, building AI models, and explaining their usefulness. Deep Learning Engineers require strong foundations in machine learning, neural networks, mathematics, and proficiency in programming languages like Python, R, and Java. They need to be familiar with data structures, software development life cycle, algorithms, and deep learning frameworks. There are similarities between Deep Learning Engineering and Software Engineering, such as the need for standard software development lifecycle management. However, Deep Learning Engineers also need to be familiar with machine learning operations (MLOps) and practices like CI/CD.