How to Become an AI Engineer

How to Become an AI Engineer


  • Building technologies and programs: Create computing technologies and programs with adaptive algorithms and deep learning capabilities.
  • Developing and managing AI systems: Develop, program, implement and manage complex AI networks and systems for specific tasks.
  • Data handling and API creation: Pull data from specific sources and create APIs to implement AI into machines.
  • Collaboration and user understanding: Work closely with users and product managers to understand technology experiences and desired outcomes.
  • Statistical analysis and improvement: Analyze data and improve AI performance through statistical analysis.
  • Transforming models into APIs: Convert machine learning models into APIs to streamline their deployment, usage and maintenance.
  • Continuous improvement: Analyze AI function and output to enhance product or service efficiency and quality.

How to become an AI engineer

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.

Step 1: Develop a strong foundation in mathematics and computer science

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.

Step 2: Gain practical experience through projects and internships

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.

Step 3: Master essential programming languages and tools

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:

  • TensorFlow
  • PyTorch
  • Keras
  • Scikit-learn 
  • OpenCV

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. 

Step 5: Develop domain-specific knowledge

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.

Step 6: Build a strong portfolio and showcase your work

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.

Step 7: Continuously learn and adapt

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.

Start your journey to becoming an AI engineer

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.



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