Artificial intelligence (AI) engineer: Learn about the role and skills needed for success

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The engineers who build and manage AI systems are increasingly valuable to companies across industry sectors. Unsurprisingly, the demand for their services outstrips the supply. 

But what is the role of an AI engineer? What are the key qualifications for the role? What really makes a good one? And how can they be made — maybe even from current developers on staff — if not found? Alternatively, how can the functionality otherwise be filled?

What is the role of an artificial intelligence (AI) engineer?

An AI engineer develops, programs, trains and deploys AI models. With 86% of companies in a recent survey reporting that AI is becoming mainstream in their businesses, the AI engineer has become a central figure.

While a data scientist focuses on finding and extracting business insights and applicable data from large datasets, an AI engineer comes from an IT infrastructure background and is charged with developing the algorithms for an AI application and integrating the application into a company’s broader tech environment. An engineer focused on algorithms may also be known as a machine learning (ML) engineer. Someone who specializes in integrating AI applications with an organization’s other technology may be known as an AI architect. Additionally, a professional specifically focused on writing code might have the title of AI developer.


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Because an important part of an AI engineer’s job is applying AI to real-world use cases, these workers must understand the problems their companies face and find ways that AI can help solve them. That often includes collaborating with other departments and teaching others about AI’s potential.

AI engineer salary and benefits

AI engineers are highly skilled. They face a wide-open job market and are well compensated. ZipRecruiter reports that the average salary of an AI engineer is upwards of $158,000 a year, with top earners offered as much as $288,000 annually. Many companies employing these professionals also offer attractive benefits for these positions. AI engineering is a reasonably future-proof career, as AI is only becoming increasingly important to everyday life. 

Education, experience and soft skills are needed for the role

AI engineers typically require expertise in three broad areas:

  1. Relevant, formal education through at least the level of a bachelor’s degree.
  2. Extensive experience in tech and/or data. 
  3. The soft skills to collaborate productively on projects with colleagues.

Although more AI-specific courses are being added to undergrad and graduate programs all the time, many AI engineers have honed their specialties with certifications or a few courses to augment their foundational degrees.

AI engineers generally need at least a bachelor’s degree in a field such as computer science, IT, data science or statistics. Some positions may even require a master’s degree. 

An advanced degree in a related area will qualify applicants for more positions. However, it may become less of a necessity over time. As the need for these workers rises, more companies are seeking experience over education.

Relevant certifications may be more useful. Taking extra AI engineer courses and exams can earn job-seekers AI-specific certifications that ensure they have the needed skills. On top of grabbing employers’ attention, these certifications will indicate an applicant has some helpful real-world experience with the day-to-day work of AI engineering.

This means a relatively broad pool of tech professionals may be candidates for a mid-career specialty in AI. Such workers, of course, must be able and motivated, and they may be found within or beyond an employer’s organization.

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10 key skills needed to succeed as an AI engineer

Let’s look at some of the more specific skills required of an AI engineer:

1. Programming language proficiency

One of the most important skills to have as an AI engineer is proficiency in at least one programming language. Ideally, applicants should have experience working with multiple languages, as some companies may prefer working in one language over another. The more diverse experience, the better.

The top programming languages in the field include:

  • Python
  • C++
  •  JavaScript
  •  Java
  • C#
  • Julia
  • Shell
  • R
  • TypeScript
  • Scala

Python is the most popular language for machine learning applications and the third most popular overall, so is often considered a default requirement for the role. Students should work with at least a few languages in their AI engineering courses, but many professionals are self-taught to at least some degree, and they have likely demonstrated proficiency with personal projects.

2. Experience with AI models

While general programming knowledge is important, engineers also need to accrue AI-specific experience. Building and training AI models is a unique practice, and those interested in an AI career seek varied opportunities to build this expertise.

Cultivating this experience is a lot like gaining proficiency in programming languages, and it is best done by working with various types of AI models, including linear regression, classification algorithms, decision trees and deep neural networks. Experimenting with different models can also help AI engineers discover what they enjoy working with the most.

Learning to work with models also resembles the programming language process. Students in AI engineering courses will build and test a few models in their studies, but personal research is valuable too. Forums and exchanges like GitHub are good places for support with AI projects.

3. Linear algebra and statistics

AI engineers need a strong grasp of applied mathematics fields such as linear algebra and statistics. Different models require an understanding of different mathematical concepts. Engineers must know how to apply derivatives and integrals to tackle gradient descent algorithms, while probability theory and Gaussian distributions are important for Hidden Markov models. A college-level mathematics education will often provide the skills necessary. 

4. Data literacy

An AI engineer’s work revolves around data, and data literacy is one of the most important skills to have when entering this field. AI engineers should be able to read, understand, analyze and apply data to various use cases.

Formal data science and statistics classes are useful, but the best practice is engaging with data projects first-hand, which is another reason why experienced tech workers may be good candidates to develop for the role. 

5. Critical thinking

“Soft” skills are also important in this field, although they are often harder to gauge. One of the most important soft skills in AI engineering is critical thinking.

AI models can be complicated, and the solution to a problem is rarely immediately evident. As a result, delivering timely and accurate results with these technologies requires a fast, creative approach to problem-solving. 

AI engineers must be able to think through multiple solutions and determine the best course of action.

6. Business acumen

A skill that is sometimes overlooked — but useful for AI engineers to have — is a strong grasp of business concepts. Operations optimization and product enhancement are the most common AI use cases for businesses, so AI engineers should understand how these processes work. Effective AI application requires an understanding of how the company operates.

AI is only as effective as its users’ ability to apply it to their end goals. Top-performing AI engineers know not just how to build functioning AI models, but also how these models can help businesses serve their unique needs. That means understanding general business concepts and company-specific considerations.

Engineers can develop their business acumen in formal courses and/or by working with colleagues in other departments.

7. Communication skills

Another crucial soft skill to have is communication. AI engineers must be able to explain to their non-technical colleagues how different AI solutions might help teams reach their goals.

A lack of understanding of how AI can benefit businesses is the second-largest barrier to adoption, according to Gartner, with 42% of chief information officers (CIOs) citing it as a problem. Knowing how to explain AI concepts will improve cooperation. 

As the technology becomes more important to a wider variety of business functions, AI engineers will work with more departments. They must be able to communicate with other workers effectively for these relationships to work. Presentation and summary skills are particularly critical.

8. Collaboration

Along those same lines, AI engineers must have excellent teamwork skills to thrive in the current market. This goes beyond telling other departments how to use AI models effectively. AI engineers must be open to feedback and cooperate with other workers to understand the challenges they face.

Many AI engineers also work in groups, even within their own departments and projects. If they are unable to work well with others, they will struggle to excel in the industry. Conversely, strong collaborative skills will help them find effective solutions faster.

Experience working in groups helps naturally build these skills, too, so prospective engineers should seek collaborative projects to improve in this area. The better they can work as part of a team, the more success they’ll have in the field.

9. Time management

Building, testing and deploying AI models is often a time-consuming process, and time management is vitally important.

 A recent study found that 83% of developers suffer from workplace burnout, with high workloads being the leading cause. While AI engineers may have little control over their workloads, they can adapt their habits to make the most of them. Of course, company culture and strong management are important for keeping such valuable professionals in peak form.

Artificial intelligence engineers should also gain experience in related technologies. Gathering relevant data and deploying AI models will likely involve working with technologies like internet of things (IoT) devices, robotics and cloud computing. Most AI projects fail, and the lack of an integrated environment is one of the most common reasons. If AI engineers hope to deploy their models effectively, working across a company’s unique IT environment is important. That means understanding the various technologies they may use.

More staffing options

The AI engineer’s role is essential and in-demand, but the AI industry is developing tools and options to enable less-specialized workers to build out applications as well:

  • Low-code and no-code options enable less-skilled staff to develop use cases. 
  • AI vendors increasingly offer prepackaged vertical and horizontal market solutions.
  • AI vendors are also cultivating business partners to offer still more prepackaged implementations.
  • Using various visual and dashboard interfaces, AI vendors are enabling non-technical business analysts to craft simple applications.
  • Consulting services are expanding to meet project-specific needs, especially.

Organizations will adapt their solutions to their size and resources, the strategic importance of their implementations and their staffing markets and philosophies, and AI skills will continue to be diffused across the wider tech environment. The role of the AI engineer is still gaining importance and will be key to many companies’ adoption of the technology.

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