What is Artificial Intelligence Engineering?
This article was originally published at Algorithimia’s website. The company was acquired by DataRobot in 2021. This article may not be entirely up-to-date or refer to products and offerings no longer in existence. Find out more about DataRobot MLOps here.
According to LinkedIn’s 2020 Emerging Jobs Report, the demand for “Artificial Intelligence Specialists” (comprised of a few related roles), has grown 74 percent in the last four years. With more companies than ever (even those outside of the tech) relying on AI tasks as part of their everyday business, demand for practitioners with this skill will only rise.
In our 2020 state of enterprise machine learning report, we noted that the number of data science–related workers is relatively low but the demand for those types of skills is great and growing exponentially.
If you’ve been curious about how to become an AI engineer or if you’re interested in shifting your current engineering role into one more focused on AI, you’ve come to the right place.
By the end of this post you’ll understand:
- The role of an AI engineer.
- The educational requirements to be an AI engineer.
- The knowledge requirements to be an AI engineer.
- The AI engineering career landscape.
What is an AI engineer?
An artificial intelligence engineer is an individual who works with traditional machine learning techniques like natural language processing and neural networks to build models that power AI–based applications.
The type of applications created by AI engineers include:
- Contextual advertising based on sentiment analysis
- Language translation
- Visual identification or perception
Is an AI engineer a data engineer or scientist?
You may be wondering how the role of an AI engineer differs from that of a data engineer or a data scientist. While all three roles work together within a business, they do differ in several ways:
- Data engineers write programs to extract data from sources and transform it so that it can be manipulated and analyzed. They also optimize and maintain data pipelines.
- Data scientists build machine learning models meant to support business decision making. They are often looking at the business from a higher strategic point than an AI engineer typically would.
What does it take to be an AI engineer?
AI engineering is a relatively new field, and those who currently hold this title come from a range of backgrounds. The following are some of the traits that many have in common.
Many AI engineers moved over from previous technical roles and often have undergraduate or graduate degrees in fields that are required for those jobs. These include:
- Computer science
- Applied mathematics
- Cognitive science
Most of the above degrees have some relevance to artificial intelligence and machine learning.
Two of the most important technical skills for an AI engineer to master are programming and math/statistics.
- Programming: Software developers moving into an AI role or developers with a degree in computer science likely already have a grasp on a few programming languages. Two of the most commonly used languages in AI, and specifically machine learning, are Python and R. Any aspiring AI engineer should at least be familiar with these two languages and their most commonly used libraries and packages.
- Math/statistics: AI engineering is more than just coding. Machine learning models are based on mathematical concepts like statistics and probability. You will also need to have a firm grasp on concepts like statistical significance when you are determining the validity and accuracy of your models.
AI engineers don’t work in a vacuum. So while technical skills will be what you need for modeling, you’ll also need the following soft skills to get your ideas across to the entire organization.
- Creativity – AI engineers should always be on the lookout for tasks that humans do inefficiently and machines could do better. You should stay abreast of new AI applications within and outside of your industry and consider if they could be used in your company. In addition, you shouldn’t be afraid to try out-of-the-box ideas.
- Business knowledge – It’s important to remember that your role as an AI engineer is meant to provide value to your company. You can’t provide value if you don’t really understand your company’s interest and needs from a strategic and tactical level.
A cool AI application doesn’t mean much if it isn’t relevant to your company or can’t improve business operations in any way. You’ll need to understand your company’s business model, who the target customers and targets are, and if it has any long- or short-term product plans.
- Communication – In the role of an AI engineer, you’ll have the opportunity to work with groups all over your organization, and you’ll need to be able to speak their language. For example, for one project you’ll have to:
- Discuss your needs with data engineers so they can deliver the right data sources to you.
- Explain to finance/operations how the AI application you’re developing will save costs in the long run or bring in more revenue.
- Work with marketing to develop customer-focused collateral explaining the value of a new application.
- Prototyping – Your ideas aren’t necessarily going to be perfect on the first attempt. Success will depend on your ability to quickly test and modify models until you find something that works.
Can I turn my current engineering role into an AI role?
Yes. Experienced software developers are well-suited to make the transition into AI engineering. You presumably have the command of more than one programming language and the foundational knowledge to learn another. It’s also likely that you’ve already worked with machine learning models in some capacity possibly by incorporating them into other applications.
If you are interested in pursuing an AI engineering role within an organization where you already work, your knowledge of the business and knowledge of how the engineering team works will be crucial.
How much does an artificial intelligence engineer earn in salary?
Artificial intelligence engineers are in high demand, and the salaries that they command reflect that. According to estimates from job sites like Indeed and ZipRecuiter, an AI engineer can make anywhere between 90,000 and 200,000 (and possibly more) depending on their qualifications and experience.
Another factor that will determine salary is location. According to the LinkedIn Emerging Jobs Report mentioned earlier, most AI engineering jobs are located in the San Francisco Bay area, Los Angeles, Seattle, Boston, and New York City.
DataRobot is the leader in Value-Driven AI – a unique and collaborative approach to AI that combines our open AI platform, deep AI expertise and broad use-case implementation to improve how customers run, grow and optimize their business. The DataRobot AI Platform is the only complete AI lifecycle platform that interoperates with your existing investments in data, applications and business processes, and can be deployed on-prem or in any cloud environment. DataRobot and our partners have a decade of world-class AI expertise collaborating with AI teams (data scientists, business and IT), removing common blockers and developing best practices to successfully navigate projects that result in faster time to value, increased revenue and reduced costs. DataRobot customers include 40% of the Fortune 50, 8 of top 10 US banks, 7 of the top 10 pharmaceutical companies, 7 of the top 10 telcos, 5 of top 10 global manufacturers.
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