How to become an AI engineer
Computers can calculate complex equations, detect patterns, and solve problems faster than the human brain ever could. Artificial intelligence (AI) is the science of making intelligent machines and computer programs. AI engineers are at the forefront of that field.
AI comprises multiple subfields, including machine learning, which is one of the ways computers acquire their intelligence. AI research scientists, machine learning scientists, and engineers search for solutions to problems, new approaches, and new technologies. The ever-changing and expanding field keeps AI engineering dynamic and impactful.
Here, we explore the role of the AI engineer and the steps required to secure a position in this industry. We look at the formal education requirements, experiential training, and additional credentials that it takes for aspiring engineers to enter the field and thrive.
What do AI engineers do?
AI engineers develop algorithms, programs, and models for machine learning and deep learning neural networks. They seek to create powerful technologies to solve problems, identify trends, and predict future events. Depending on their role and focus, AI engineers may have numerous responsibilities, including:
- Software development
- Project design and management
- Creating machine learning algorithms
- Automating processes and infrastructure
- Developing and testing models
- Managing teams
- Monitoring and analyzing metrics and progress
- Discussing projects with stakeholders
AI engineers work with AI research scientists, machine learning scientists, data scientists, programmers, and business analysts. They typically use the Python programming language. AI engineers also use platforms like Google's Vertex AI, Amazon Web Services, IBM Watson Studio, and GitHub Copilot.
Considerations before starting an AI engineer career
The AI engineer profession has plenty to get excited about, but you must know more about the field before diving in. Here are a few things you should know before getting started on the path.
- Fortune Business Insights projects over 33% growth for the AI industry between 2021 and 2028, growing the market from $47.47 billion to $360 billion.
- Many AI engineers work remotely, a growing trend that could improve AI adoption within organizations.
- According to PayScale, the median annual wage for machine learning engineers was approximately $112,000 in 2021.
- While AI engineers typically need a bachelor's degree, they can advance and specialize in their training via master's and Ph.D. degrees, coding bootcamps, and self-study.
- As technology continues to evolve, professionals may need regular continuing education to update their skills and expertise.
- AI research scientists and engineers work in teams and collaborate with other AI professionals, which requires strong communication and interpersonal skills.
- While many AI engineers come from the computer engineering branch, the field may also intersect with mechanical, industrial, and electrical and electronics engineering.
- Engineering can be a challenging academic discipline that demands commitment and effort from successful students.
How do I become an AI engineer?
AI engineers can take multiple paths to the profession, but there are minimum field requirements and expectations that they need to complete along the way. Here, we outline the steps it takes to enter the field, including the necessary education, projects, experiences, specializations, and certifications.
1. Learn skills through degree(s), coding bootcamp(s), or self-study.
The AI engineering field attracts professionals from numerous educational backgrounds. While most entry-level positions require a bachelor's degree at minimum, the discipline is flexible and may include computer engineering, data science, computer information systems, and a computer science degree. Ensure any computer science or engineering programs have received accreditation from the Accreditation Board for Engineering and Technology (ABET).
In addition to a degree, you can build up your AI engineering skillsets via bootcamps, such as an AI or machine learning bootcamp, a data science bootcamp, or a coding bootcamp. These condensed programs usually provide much of the required training for entry-level positions. They typically take less than one year to complete.
You can develop AI engineering skills through self-study as well. You may conduct your own AI research and take machine learning and deep learning classes. You should consider picking up popular programming languages, including:
- Python
- Java
- C++
- R
- Prolog
- Lisp
- Julia
2. Take on projects, practice skills, and build your portfolio.
In the tech world, employers want job candidates with diverse resumes and portfolios. While in school, you can build up your portfolio with class assignments or internship projects. Portfolios can highlight many skills, but you should showcase your ability to think outside the box and add value to society.
While filling out your portfolio and taking on new experiences, consider projects that demonstrate a wide range of skills. For example, you may look at projects that specialize in analysis, translation, detection, restoration, and creation. Gaining experience and building a robust portfolio are great ways to advance your tech career.
3. Pursue internship(s).
Internships are incredibly useful for AI engineering students. These placements provide an excellent environment for career preparation, practical training, resume building, and professional networking. In addition to developing relationships that could turn into full-time postgraduate employment, interns get to test out various types of jobs, organizations, and specializations.
The best internships in the AI engineering field depend on the individual student and their specific career goals. For example, learners might consider popular field specializations, such as smart technology, automotive systems, and cybersecurity. When choosing an internship, focus on the AI engineering skills you need to satisfy your long-term goals, such as programming, machine and deep learning, or language and image processing.
4. Determine your AI scientist career path.
As the number of AI applications increases, so do the number of organizations and industries hiring AI engineers. In addition to information technology, AI engineers work in manufacturing, transportation, healthcare, business, and construction. They specialize in robotics, disease detection, security, and self-driving cars.
Even within these industries and specializations, the AI engineer role can vary. They may work as research scientists in AI, robotics engineers, program developers, or machine learning scientists. They can specialize in human-computer interactions, human vision, or business intelligence.
5. Earn AI engineer certification(s).
The functions of a professional certification depend on the credential. While no mandatory licensure or certification is required for AI engineers, a professional certification can significantly improve a candidate's employment and advancement opportunities. They demonstrate the credential-holder has substantial training, experience, and a solid grasp on the material.
Most certifications feature a minimum requirement for education and experience. Candidates also need to pass an examination to demonstrate their level of expertise. Here are the best IT certifications for AI professionals:
- Certified artificial intelligence engineer from the United States Artificial Intelligence Institute
- Azure AI fundamentals certification from Microsoft
- Professional machine learning engineer from Google
- Certified artificial intelligence professional from Certified Artificial Intelligence Professional Inc.
- Professional engineer (PE) certification from the National Society of Professional Engineers
6. Create your resume and apply for jobs.
Every employer looks for something unique in resumes, but there are tried and true methods for making sure a resume gets noticed. AI engineers need to tailor their resumes to the positions and organizations they are applying to. They should emphasize all relevant roles while limiting the document to two pages.
Aspiring AI engineers should also highlight all relevant technical skills, such as their programming languages, experience with models and algorithms, knowledge of data engineering and analysis, and experience with different services and platforms. Entry-level candidates typically apply for junior, assistant, and associate roles.
The interview process varies by role and employer, though they typically feature multiple stages. Interviewers often examine the applicant's past work experience. Interviews also include coding and algorithm questions to test the candidate's knowledge.
In conclusion
The enormous growth in AI and machine learning has provided AI engineers with professional flexibility and opportunity. To enter the field, you can pursue multiple forms of training, build a portfolio, practical exercises, certifications, and resume-building approaches. Use this guide as a resource to help you get on the right path and find your way into the AI industry.
This article was reviewed by Brian Nichols
Born and raised in upstate New York, Brian Nichols began his IT education through a vocational high school where he focused on computer science, IT fundamentals, and networking. Brian then went to his local community college, where he received his associate of science in computer information science. He then received his bachelor of science in applied networking and system administration from a private college. Brian now lives in Kansas City, where he works full-time as a DevOps engineer. Brian is also a part-time instructor in cybersecurity. He's passionate about cybersecurity and helping students succeed.
Brian Nichols is a paid member of the Red Ventures Education freelance review network.