An artificial intelligence engineer develops intelligent algorithms to create machines capable of learning, analyzing, and predicting future events.
Artificial intelligence engineering is growing as companies look for more talent capable of building machines to predict customer behavior, capitalize on market trends, and promote safety. This makes it an ideal career path for those with specialized interests and skills.
In this guide, we’ll take a deeper dive into the role of an artificial intelligence engineer, including a look at the recommended skills and background and steps needed to become an artificial intelligence engineer.
What is an Artificial Intelligence Engineer?
Artificial intelligence engineers develop theories, methods, and techniques to develop algorithms that simulate human intelligence. Companies use artificial intelligence to improve their decisions and production strategy. Today’s machines can process large volumes of data accurately and quickly.
Artificial intelligence engineers can further specialize in machine learning or deep learning. While machine learning is based on decision trees and algorithms, deep learning is based on neural networks.
A common application of artificial intelligence is predicting consumer preferences in retail stores and online environments. Amazon.com has predictive algorithms to examine a shopper’s behavior and suggest similar items for purchase. The shopper is shown these items before the point of purchase which may result in increased sales for the retailer.
Similarly, artificial intelligence can prevent drivers from causing car accidents due to judgment errors. This can save lives and promote safety on the roads. Popular products within artificial intelligence include self-driving cars, automated financial investing, social media monitoring, and predictive e-commerce tools that increase retailer sales.
The artificial intelligence engineer’s role goes beyond basic computer programming. Engineers are expected to develop programs that enable machines and software to predict human behavior based on past actions and individualized information.
An artificial intelligence engineer generally has an analytical approach with in-depth knowledge in computer science, programming languages, mathematics, statistics, and common AI tools like Scikit Learn, Tensorflow, Theano, Caffe, MxNet Keras, PyTorch, and CNTK. Engineers use these software development tools to create new programs that will meet the unique needs of the company they work for.
Within this role, artificial intelligence engineers are responsible for developing, programming, and training the complex algorithms that allow AI to perform like a human brain. This role requires experience in software development, programming, data science, statistics, and data engineering.
Artificial intelligence developers identify and synthesize data from various sources to create, develop, and test machine learning models. AI engineers use application program interface (API) calls and embedded code to build and implement artificial intelligence applications.
Artificial Intelligence Degree
Artificial intelligence engineers are expected to have a bachelor’s or master’s degree in computer science, data science, mathematics, information technology, statistics, or finance.
AI engineers typically understand statistics, linear algebra, calculus, and probability because AI models are built using algorithms based on these mathematical fields. Some of artificial intelligence’s most common machine learning theories are the Naive Bayes, Hidden Markov, and Gaussian mixture models.
AI engineers will also need to understand common programming languages, like C++, R, Python, and Java. Most artificial intelligence models are developed and deployed using these programming languages.
Many schools provide students with a master’s degree or specialization in artificial intelligence, including Carnegie Mellon University, Stanford University, Massachusetts Institute of Technology, Cornell University, UCSD, Georgia Institute of Technology, and the University of Southern California.
These programs combine artificial intelligence courses with advanced mathematics, data science, and analytics knowledge. Regardless of the program, most master’s level degrees allow students to get hands-on experience with computer science, artificial intelligence, and data analytics, which are foundational concepts to an artificial intelligence career.
In addition to degrees, there are also bootcamps and certifications available for people with related backgrounds and experience.
How to Become an Artificial Intelligence Engineer
The first step towards becoming an artificial intelligence engineer is building up the required knowledge and experience. Employers typically look for two things here:
- Education: Many job descriptions for artificial intelligence engineers require applications to hold a bachelor’s degree or higher in mathematics and basic computer technology. A bachelor’s degree represents the applicant’s willingness to learn more in this career path, while a master’s degree demonstrates a familiarity with advanced theory, modeling, and project management. Although a strong candidate may obtain an artificial intelligence engineering position with a bachelor’s degree, many employers prefer applicants with a master’s degree in computer science.
- Experience: Many employers want to see some evidence of experience with programming languages, especially Python, R, and Java. Linear algebra, statistics, signal processing techniques, and neural network architectures will also make a candidate attractive. Internships or participation in events like bootcamps or hackathons can also be used to demonstrate experience. Although there is an increasing number of artificial intelligence job candidates learning prerequisite information on their own, a master’s degree from an accredited institution is the recommended way to meet specialized hiring standards and gain real-world experience from industry experts.
- Artificial intelligence tools: The top tools used for artificial intelligence development include Scikit Learn, Tensorflow, Theano, Caffe, MxNet, Keras, PyTorch, and CNTK. Artificial intelligence engineering candidates should be proficient in at least one of these tools, preferably the one listed in the job advertisement.
- Development and design methodologies: Artificial intelligence engineers are expected to convert machine learning models into application program interfaces so that other applications can use them. Artificial intelligence engineers must build AI models from scratch and help others understand the model’s results. AI engineers build data ingestion and data transformation infrastructure, and they automate the infrastructure that the data science team uses. Performing statistical analysis and fine-tuning the results is part of the job. Finally, AI engineers must set up and manage product infrastructure over the product’s life.
The top-rated companies hiring artificial intelligence engineers in the United States are:
- J.P. Morgan
- Wells Fargo
As you can see, the primary employers are in technology, consulting, retail, and banking. A solid understanding of consumer behavior is critical to most employees working in these fields. We can expect to see increased AI applications in transportation, manufacturing, healthcare, sports, and entertainment. Upcoming products include self-driving cars, production robots, autonomous surgical robots, dosage error reduction, custom-tailored movie suggestions, advertisements, and athletic performance forecasts.
It can be challenging for candidates to prove that they have the required experience and skills during an interview. To help them gauge a candidate’s experience, employers typically look for:
- Portfolio: Employers are increasingly looking for samples of an applicant’s work during the interview process. Having a Github repository with past artificial intelligence modeling can help candidates obtain an artificial intelligence engineer job.
- Certifications: Like many technology roles, artificial intelligence engineers are expected to engage in continuing professional education. Earning certifications and demonstrating skills with relevant platforms, languages, and design methodologies can help meet these needs when developing new skill sets.
Hard skills are not enough to be successful as an artificial intelligence engineer. Artificial intelligence engineers also need certain soft skills, such as:
- Critical thinking: Artificial intelligence engineers are frequently responsible for understanding and analyzing problems. AI engineers often need to deconstruct each aspect of the data, identify minor issues, and implement algorithmic changes to improve their models. It is also important to understand the user’s needs and devise solutions accordingly.
- Creativity: Artificial intelligence engineers need to be able to innovate to create software capable of meeting specialized needs. Creativity involves looking at problems from different perspectives and coming up with new solutions.
- Collaboration: Due to the complex nature of artificial intelligence modeling, the work often involves multiple professionals. Different professionals may be uniquely skilled in data science, interaction design, computational science, computer and information research, or software development. The ability to collect data and communicate information with a variety of team members is essential for the success of the AI project.
What Does an Artificial Intelligence Engineer Do?
At the core, the job of an artificial intelligence engineer is to create intelligent algorithms capable of learning, analyzing, and reasoning like the human brain. However, this can include a few different responsibilities, such as:
- Analysis: AI engineers may be responsible for analyzing and employing artificial intelligence principles regarding reasoning and uncertainty.
- Image reconstruction: Artificial intelligence engineers often apply AI and machine learning techniques for image analysis and reconstruction.
- Development: Artificial intelligence engineers develop solutions that model human behavior to accomplish complex tasks and processes.
- Machine learning: AI engineers create solutions that combine the best practices of artificial intelligence with principles from machine learning.
- Improving systems: Artificial intelligence engineers are responsible for developing and applying algorithms to improve existing AI software, especially when the software deviates from the end user’s expectations or needs.
Not all artificial intelligence engineers perform all of these roles. A job’s responsibilities often depend on the organization and the industry to which the company belongs.
Artificial Intelligence Engineer Career Outlook and Salary
Artificial intelligence engineering is a career path that is always in demand. As organizations become increasingly reliant on computers as part of their daily business, they need people to apply logic, probability analysis, and machine-learning concepts to solve problems (check out this hiring guide for more details).
The Bureau of Labor Statistics (BLS) predicts a 22 percent growth in the field between 2020 and 2030, which is “much faster than average.”
The BLS does not specifically track artificial intelligence engineers, but it does have information on computer and information research scientists.
An artificial intelligence engineer’s profile is comparable to a computer and information research scientist’s. Regardless of title, applicants for each role will benefit from having a master’s degree or higher in computer science or a related field.
According to the BLS, the median pay for computer and information research scientists in 2020 was $126,830 per year or $60.97 per hour. This is generally with a master’s degree and the median years of work experience required by current job listings, so candidates with a higher degree or greater experience can likely expect higher salaries.