How To Become an AI Engineer: A Comprehensive Guide

In a recent AWS survey on AI skills in the United States, 73% of employers revealed that they prioritize hiring talent with AI skills and experience. Here’s the catch — nearly three in four of these employers struggle to find the right fit for their needs.

As the demand for AI-driven solutions escalates, so does the need for skilled AI engineers. If you’re interested in pursuing a career at the forefront of technological innovation, becoming an AI engineer might be the path for you.

In this comprehensive guide, we’ll navigate how to become an AI engineer, exploring the necessary skills, educational background, practical experience and career opportunities in this dynamic field.

At its core, the role of an AI engineer revolves around designing, developing and deploying AI systems to tackle complex problems. Whether it’s building predictive models, natural language processing algorithms or computer vision applications, AI engineers play a pivotal role in harnessing the power of data and machine learning. 

It’s essential to differentiate between AI engineers, data scientists and machine learning engineers, as each role encompasses distinct responsibilities and skill sets. Data scientists focus on extracting insights from data, and machine learning engineers specialize in implementing machine learning algorithms. AI engineers possess a broader skill set, combining elements of both disciplines to create intelligent systems.

AI engineers are in high demand across a wide range of industries, including technology, healthcare, finance, retail and manufacturing. Companies are increasingly recognizing the value of AI to improve their operations, according to a Forbes Advisor survey.

As an AI engineer, you’re tasked with designing, developing and deploying AI systems to solve complex problems. Key responsibilities of an AI engineer include:

Starting your journey to becoming an AI engineer offers many possibilities, each with its own opportunities for growth. While the steps outlined here provide a roadmap, it’s important to remember that the field of AI offers diverse routes to success.

To excel as an AI engineer, it is often helpful to have a solid understanding of mathematics and computer science principles. AI algorithms rely heavily on mathematical concepts such as linear algebra, calculus, probability and statistics. Additionally, proficiency in computer science fundamentals, including data structures, algorithms and programming languages, is recommended.

One way to start is by obtaining a bachelor’s degree in a relevant field such as computer science, mathematics, electrical engineering or a related discipline. During your studies, you will likely focus on courses that cover machine learning, deep learning, natural language processing, computer vision and other AI-related subjects.

Theoretical knowledge is crucial, but practical experience is also important when becoming an AI engineer. Actively seek opportunities to work on AI projects and participate in internships or co-op programs. These hands-on experiences will allow you to apply your knowledge, develop problem-solving skills and gain exposure to real-world AI challenges.

Consider contributing to open-source AI projects, participating in hackathons or coding competitions. Developing your own personal projects can also be a great opportunity to experiment with tools while working on something you’re particularly interested in. Activities like these enhance your technical skills, as well as demonstrate your passion and dedication to potential employers.

AI engineers use programming languages and tools to develop their programs. Becoming proficient in a relevant language and its libraries will be vital to being able to create and implement your ideas. It may be helpful to know:

Familiarize yourself with popular AI frameworks, libraries and tools. Understanding their strengths, weaknesses and appropriate use cases can be helpful in effectively tackling AI problems. Frameworks and libraries include:

The AI field is constantly evolving, with new techniques, algorithms and applications emerging regularly. A prime example of this rapid evolution is the GPT model by OpenAI, first launched in June 2018. Since then, it has undergone multiple iterations, each bringing improvements in language understanding, generation and contextual reasoning.

To remain competitive as an AI engineer, staying current with the latest trends, research papers and industry developments is crucial. There are various ways to stay updated, including attending conferences, workshops and meetups, as well as regularly reading industry publications and blogs. 

While AI engineers need a strong foundation in mathematics, computer science and programming, they can also benefit from developing domain-specific knowledge in areas where AI is applied. For example, understanding medical terminology, practices and challenges can be helpful if you’re interested in working on AI solutions for healthcare.

Similarly, if you’re passionate about applying AI to finance, acquiring knowledge of financial markets, trading strategies and risk management might give you a competitive edge. By combining AI expertise with domain-specific knowledge, you can better understand the problems you’re solving and develop more effective and practical solutions.

As an AI engineer, having a compelling portfolio is essential for showcasing your skills and achievements. Document your projects, including the challenges you faced, the techniques you employed and the results you achieved. Consider creating a personal website or an online portfolio to showcase your work and make it easily accessible to potential employers.

The field of AI is rapidly evolving, and what is cutting edge today may become outdated tomorrow. As an AI engineer, embracing a mindset of continuous learning and adaptation is crucial. Stay curious, explore new techniques and methodologies and be willing to step outside your comfort zone.

Additionally, consider pursuing advanced degrees, such as an online Master of Science in Artificial Intelligence or a Ph.D., to deepen your knowledge and contribute to advancing AI research. Continued education not only enhances your expertise but also ensures that you remain at the forefront of innovation in the dynamic field of artificial intelligence.

Remember, there is no one-size-fits-all path to becoming an AI engineer. Flexibility, adaptability and a willingness to embrace new challenges are equally essential traits to cultivate along the way. Whether it’s pursuing formal education, participating in online courses or engaging in real-world projects, every experience contributes to the development of expertise and proficiency.

If you’re ready to embark on an exciting career as an AI engineer, consider enrolling in Maryville University’s online Master of Science in Artificial Intelligence or AI certificate program. Through focused higher education and training in this future-oriented field, you can contribute to driving innovation and shaping the future of technology.