# Artificial Intelligence Engineer ## AI Engineering AI Engineering (AIE) is a field of research and practice that combines the principles of systems engineering, software engineering, computer science, and human-centered design to create AI systems in accordance with human needs for mission outcomes. A successful AI engineer needs a thorough knowledge of applied mathematics, computer science, and software engineering as well as experience developing software solutions for a variety of real-world problems. AI/ML is not about algorithms, APIs, or frameworks. > Any competent software engineer become proficient with any software development tool. In a nutshell, it's not about code; it's knowing the theory and best practices for AI, especially the limitations. The need for a mature engineering discipline to guide AI capabilities is urgent. To realize the benefits of AI for such scenarios, we must successfully meet the challenges unique to AI systems. While the capability to develop AI systems has risen due to available computing power and datasets, these systems often work only in controlled environments and are difficult to replicate, verify, and validate in the real world. AI Engineering aims to provide a framework and tools to proactively design AI systems to function in environments characterized by high degrees of complexity, ambiguity, and dynamism. SEI has developed three pillars to guide our approach to AI Engineering: Human-centered AI Key to the implementation of AI in context is a deep understanding of the people who will use the technology. This pillar examines how AI systems are designed to align with humans, their behaviors, and their values. Scalable AI Effective AI systems require large investments of time and money to develop. This pillar examines how AI infrastructure, data, and models may be reused across problem domains and deployments. Robust and Secure AI One of the biggest challenges facing the broad adoption of AI technologies and systems is knowing that AI systems will work as expected when they are deployed outside of closely controlled development, laboratory, and test environments. This pillar examines how we develop and test resilient AI systems. ## What is an AI Engineer? An AI Engineer is a type of software engineer specializing in development of AI/ML applications. An AI Engineer needs to have a thorough understanding of the core software engineering concepts (SWEBOK) as well as the full software development life cycle for AI/ML applications which has some differences. > In a nutshell, when you create a program to solve an AI problem you are performing AI engineering. ## Lewis University ARTIFICIAL INTELLIGENCE, Master of Science A Master of Science in Artificial Intelligence degree equips students with in-depth knowledge and practical skills needed to build and implement modern AI systems. The degree provides the necessary theoretical background in machine learning and artificial intelligence, knowledge related to the application of artificial intelligence in computing systems and the ethical implication in the development and use of artificial intelligence. A graduate of the MSAI program will be prepared for a wide range of careers where artificial intelligence is applied including work in industry or the government, or further graduate studies. ### PROGRAM GOALS AND STUDENT LEARNING OUTCOMES The goals and objectives of the M.S. in AI program are:DOTNET_ROOT= - Research and apply current state-of-the-art theories and scientific methods related to artificial intelligence to solve real-world problems. - Demonstrate the approach of Responsible AI where the ethical implications, risks, and responsibilities of AI solutions should be evaluated within global and societal contexts. - Design AI systems that are socially responsible, ensuring that AI applications benefit society as a whole. ## References [1]: [AI Engineering](https://insights.sei.cmu.edu/artificial-intelligence-engineering/) [2]: [Lewis University ARTIFICIAL INTELLIGENCE, M.S.](https://www.lewisu.edu/academics/msai/)