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Artificial Intelligence Engineer: An Emerging Career Path for Success

Published on: Oct 11, 2022
Last Updated on: Apr 1, 2023
By: Editorial Staff
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Artificial intelligence is growing, and not just in the research labs of the world’s leading universities. Increasingly, businesses are harnessing the power of artificial intelligence and machine learning to optimize their operations and deliver better products to consumers. If you’ve ordered an Uber, browsed Netflix, or added something to your cart on Amazon, you’ve experienced first-hand the impact artificial intelligence can have on our daily lives. In the coming years, this impact will only grow: according to Stanford University’s 2022 AI Index Report, private investment in artificial intelligence in 2021 doubled that of 2020, topping $90 billion worldwide.

To translate this investment of capital into the practical application of AI technologies, companies are increasingly turning to professionals working in the fledgling field of artificial intelligence engineering. Regardless of whether their job title is AI engineer, machine learning engineer, artificial intelligence specialist, or even software engineer, AI engineering professionals focus on ensuring that technical capabilities in the lab can be implemented in the real world.

This pursuit can be lucrative: Glassdoor pegs the median artificial intelligence engineer salary at $119,640 annually. For a machine learning engineer, the median salary climbs to $124,040. Compare these to the average annual salary in the US, which the U.S. Bureau of Labor Statistics estimated to be $58,260 in 2021, or the average annual salary for a web developer, which Indeed pegs at $71,707.

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Is artificial intelligence engineering a career path for you?

If helping to implement cutting-edge technology sounds like an exciting career opportunity, you’ve come to the right place. In this article, we’ll elaborate on what exactly artificial intelligence engineering is as well as some common career paths for AI engineers. We’ll also cover what skills are needed to succeed as an AI or machine learning engineer and how you can get started building and honing them in a bootcamp, online bachelor’s degree, or master's program.

What is artificial intelligence engineering?

According to Carnegie Mellon University’s Software Engineering Institute (SEI), AI engineering is “an emergent discipline focused on developing tools, systems, and processes to enable the application of artificial intelligence in real-world contexts.” This goal of reconciling the virtual with the real means that AI engineering involves multiple disciplines, including, according to SEI, systems engineering, computer science, and human-centered design.

Since AI engineering is so new, different people will offer different accounts of what the guiding principles should be. That said, the SEI’s three pillars of AI engineering are instructive as to some of the problems an artificial intelligence engineer encounters every day:

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Carnegie Mellon’s SEI believes AI engineers must ensure that the technologies they develop align with the values, behaviors, and goals of the humans that will use them.
Human-Centered AI
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Acknowledging the time and expense it takes to develop AI systems, CMU’s SEI argues that such systems ought to be designed in ways that allow for scalability and application across different industries and use-cases.
Scalable AI
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With the knowledge that the lab is very different from the real world, CMU’s SEI prioritizes the development of safety mechanisms to ensure AI technologies will work as intended, regardless of the environment.
Robust and Secure AI

AI engineering is about making emerging AI technologies work for everyday people and everyday life.

Of course, it’s not just academics pushing for human-centered AI solutions. As artificial intelligence models become more advanced, pioneers in industry are also intent on making them more useful and useable. Shervin Kodabandeh, co-leader of Boston Consulting Group’s AI initiative, BCG GAMMA, believes that only 10% of the companies that collectively spend billions on AI every year are seeing a return on this investment. The reason behind these companies’ success, Kodabandeh argues, is that they are focused not on replacing human employees with AIs, but rather on cultivating symbiotic relationships between humans and AIs — a focus, in other words, on AI engineering.

How are people making a difference through AI Engineering?

If the idea of working towards harmonious collaboration between humans and AIs has piqued your interest, there’s good news: there are a variety of career paths for someone interested in artificial intelligence engineering. Perhaps counterintuitively, however, many don’t involve the title “AI engineer.” Though you can find ample descriptions of the responsibilities of an artificial intelligence engineer online, finding job postings with that title is more difficult. Some companies do staff AI engineers, but more often someone working to implement real-world AI solutions will have a job title like “machine learning engineer,” “software engineer,” “AI specialist,” or even “data scientist.”

In the end, different companies simply use different titles for the kind of work an AI engineer does — even if there is a lot of overlap in the responsibilities of the various positions. And then, of course, there are the more specialized AI engineer positions like “robotics engineer,” “big data engineer,” and “business intelligence developer.”

You can find deep dives on many of these different AI engineering positions in the Jobs section, but here, we’ll focus on some real-world examples of how artificial intelligence engineers at different stages of their careers might impact key industries like healthcare, manufacturing, and e-commerce.

AI Engineer - Computer Vision/Health Imaging @ Covera Health

At Covera Health, an entry-level AI engineer working in computer vision and health imaging has the opportunity to leverage AI solutions to ensure accurate diagnosis within the field of radiology, ensuring people have access to quality care regardless of location. 

Key responsibilities include:

  • Consulting on Amazon Web Services (AWS) platform optimization

  • Designing, developing, and shipping AI models to maintain and improve existing analytics

  • Communicating results to internal and external stakeholders

Requirements include:

  • Bachelor’s or master’s degree in computer science or nearby field

  • 1-3 years of experience (including internships) as a software or AI engineer

  • Experience with SQL, a programming language like Python or C++, and Unix environments

  • Experience with machine learning models, deep learning pipelines and, ideally, deep learning environments inference engines like TritonRT

Machine Learning Engineer - Manufacturing @ Apple

At Apple, an experienced machine learning engineer working in manufacturing has the opportunity to optimize supply-chain logistics and the manufacturing process for iPhones, Macs, and other products through deployment of novel machine learning algorithms and systems.

Key responsibilities include:

  • Writing ML algorithms to improve Apple’s supply chain and manufacturing processes

  • Designing, developing, and deploying machine learning systems to Apple’s factories worldwide

  • Collaborating with software engineers, hardware engineers, machine learning engineers, and operations teams

Requirements include:

  • Master’s or PhD in Computer Science or related STEM field

  • Advanced hands-on experience writing machine learning algorithms and developing machine learning models

  • Experience with common ML tools like PyTorch, Apache Spark, TensorFlow

  • Advanced experience in a programming language like Python or R

  • Previous experience developing ML systems in manufacturing settings a plus

Software Development Engineer - AI Natural Language @ Amazon

At Amazon, a software development engineer working in natural language processing has the opportunity to develop systems that improve customer experience with “conversational AI”: Alexa.

Key responsibilities include:

  • Collaborating with machine learning scientists on overall conversational AI strategy

  • Designing, developing, and shipping new AI models to improve Alexa’s capabilities

  • Improving system architecture and best practices

Requirements include:

  • Bachelor’s degree in computer science or electrical engineering

  • Facility with programming languages (Python, C++, etc.)

  • Experience with system architectures, software infrastructure, cloud software services

  • Experience with AI systems a plus

  • Ability to successfully communicate with users and other teams/stakeholders

While each of these positions has different responsibilities and impacts, there are striking similarities in the kinds of skills needed to work in AI or machine learning engineering. In the next section, we’ll summarize what exactly these skills are before we suggest some ways that you can acquire them to start your journey to become an AI engineer.

What skills do you need to work as an AI engineer?

Whether you’re working on radiology diagnostics at Covera, manufacturing efficiency at Apple, Alexa at Amazon, or at one of the thousands of other companies currently employing AI engineers, you’ll be expected to:

  • have a firm grasp on the fundamentals of software engineering

  • be fluent in at least one programming language (and usually more)

  • have experience writing AI algorithms and developing AI and machine learning models 

Even for entry-level jobs, companies usually want their AI engineers to come in having already had on-the-job experience where they’ve practiced these skills, even if only for an internship.

While hard skills are certainly essential, each of the jobs we’ve looked at also places significant emphasis on soft skills. This is understandable because AI engineering is not just about building impressive technologies, but ensuring these technologies are designed with humans at the forefront. Working as part of a cross-functional team, an AI engineer communicates complex ideas — for example, the concept of a deep learning neural network — in ways understandable to a variety of professionals. A typical meeting could include a product manager, a data scientist, and non-technical stakeholders. Oftentimes, AI engineers are also asked to develop subject-matter knowledge, engage with user feedback to gain a better understanding of any pain points, or lead teams of other engineers. 

How can you develop skills to succeed as an AI engineer?

As evident from each job description we’ve presented, the most common way into AI engineering is through a bachelor’s degree in computer science that allows you to specialize in artificial intelligence and machine learning. For many entry-level jobs, this will give you sufficient training in the basics of software engineering and skill in programming languages, as well as experience with the tools needed to write AI algorithms and build AI models. 

If you’re interested in this route, pop over to our recommendations for cutting-edge online bachelor’s degree programs in computer science, artificial intelligence, and machine learning.

Sometimes, as with the Apple job above, companies only interview candidates with a master’s degree or a PhD. Graduate study in artificial intelligence or machine learning allows you to deepen your knowledge in areas like deep learning, computer vision, and robotics—knowledge that will make your resume stand out from the pile and gain you access to jobs with better compensation and higher impact.

If you already have a bachelor’s degree in computer science or a related STEM field and are looking to unlock your next big career opportunity, head over to our round-up of the best online AI and online ML graduate programs.

Though it certainly helps, a degree program isn’t the only way to become an AI or machine learning engineer. Increasingly, students and those looking to start down a new career path are opting for artificial intelligence, data science, or even machine learning bootcamps

If you’re looking for a more flexible, less expensive way into AI engineering, check out our ML bootcamp recommendations to find an option that works for you.