How to Become an Artificial Intelligence Engineer – A Complete Career Guide
Without highly skilled engineers, infrastructure like bridges and skyscrapers would fall quickly into disrepair. Likewise, today’s technologies require qualified professionals to create, test, and maintain their always evolving, complex programs. And in the constantly developing field of AI, artificial intelligence engineers build automated frameworks to improve performance in just about every industry.
Every day, AI experts create and maintain chatbots for websites, software for information security systems, virtual assistants for financial firms and consumers, and all sorts of technologies that mimic the complex workings of the human brain. They are the vanguard of a technological revolution.
A career in AI is rewarding, creative, educational, and full of opportunities. This career guide highlights what the role encompasses, how to join the field, and examples of its real-world importance in our everyday lives.
- What is an AI Engineer?
- What Does an AI Engineer Do?
- Industries Requiring AI Engineers
- AI Engineer Skills
- AI Engineer Requirements
- AI Engineer Job Description
- Entry-level Artificial Intelligence Jobs
- AI Engineer Salary
- AI Engineer Job Growth
- How To Become an Artificial Intelligence Engineer in Four Steps
- Start a Career in Artificial Intelligence Engineering
What Is an AI Engineer?
In the field of AI, engineers are responsible for turning simple computer programs into complex working models of the human brain. To do this, the engineer trains software to use a number of AI-driven technologies. This produces complex programs that recognize patterns, predict future trends, and solve intricate problems like a human would but with much more efficiency and consistency.
For this to be possible, an artificial intelligence developer should be an expert in one or more artificial intelligence systems:
- Machine learning
- Deep learning
- Neural networks
- Natural language processing
- Voice recognition
- Intelligent robotics
- Computer vision
Artificial intelligence developers don’t simply make software more advanced. They employ machine learning algorithms to bolster AI systems as a framework for innovating and problem-solving. In the most complex scenarios, they train multiple kinds of AI, across dynamically running programs, to work together towards a common objective.
AI Engineers Are Informed Collaborators
Simultaneously running artificial intelligences require coordination between multiple teams. While typically having a background in data engineering and programming, they often depend on the expertise of coworkers across teams to complete those jobs. Even though there may be some overlap in responsibilities, AI developers focus primarily on piecing together the different components that create a working AI program.
To illustrate this concept, take for example the number of teams it takes to run a successful marketing campaign. To start, data science marketing analysts gather important market and consumer behavior statistics. The graphic design team works with the marketing team on creative ads and ad placement. To make it all come together, the financial experts in the company allocate funds and monitor the effectiveness of the entire project.
Each step is full of repetitive, complex tasks that must be done before the project moves forward. Through the lens of an AI developer, those tasks can be fully automated. A trained AI can put ads on websites that have historically performed well for advertising certain products. By analyzing past campaigns and market trends, artificial intelligence programs can also make budgetary recommendations.
To keep an AI-driven marketing campaign moving forward, the AI programmer works continuously with every team. The different teams feed the engineer ideas and data. The engineer then applies those ideas to multiple campaigns and trains the AI program to repeat tasks with astounding speed and accuracy. Unburdened by the monotonous yet time-consuming jobs the AI program completes, everyone involved has more bandwidth and energy to focus on innovative, creative endeavors.
The Difference Between AI Engineers, Machine Learning Engineers, and Other Subsets of Technology Engineers
People in the early stages of learning about AI may be confused about the number of terms used to describe the industry. Newcomers may be curious about AI systems and wonder which ones are actually artificial intelligence and which ones aren’t.
As tech giant IBM points out, all of those systems are subfields of AI. Machine learning is a type of artificial intelligence. A neural network is, in turn, a type of machine learning. Think of an AI technician or engineer as a person who coordinates all of those systems in one dynamic package.
For example, when a video streaming platform recommends videos, it’s using machine learning. Based on input from different users, it reliably guesses what videos to play next. That’s all it does, but it does its job quickly and correctly for every user.
That’s similar to but a little less complex than a virtual assistant. To operate, a virtual assistant has to interpret a person’s voice, respond, and employ other applications to accomplish a task. It often speaks and operates a smartphone or other device like a person would. While it may use machine learning techniques, it has to process different kinds of data and bring them all together in a human-like presentation.
AI technicians are responsible for that meeting of disciplines. That’s not to say that a machine learning engineer isn’t an advanced role. In fact, engineers specializing in specific AI systems are also AI experts. In the broader sense, though, top-level artificial intelligence architects have a wider understanding of the field that is essential to integrating different forms of artificial intelligence to complete projects and improve performance.
Data Science Is the Foundation of AI Engineering: Examples in Finance and Military Applications
In a way, AI systems act as virtual data scientists. They procure data, analyze it, and use that analysis to make informed decisions and predictions. However, that doesn’t mean an AI specialist lets software do the heavy lifting when it comes to handling data. It actually means quite the opposite.
Take cybersecurity, for instance. An artificial intelligence technician in this field is responsible for creating human-like applications that can identify threats and protect data on a large scale. For that to be possible, the engineer must train information-guarding programs to recognize innocuous actions by approved users, identify if a threat is a human or another AI system, and take the appropriate actions.
According to data scientist Dr. Kat Campise, this can be quite a fragile situation and emphasizes the importance of data science to any application of artificial intelligence. She highlights how in the financial industry, a cybersecurity AI can’t simply stop all transactions until threats are dealt with. It must know which transactions to halt and allow other business to continue as usual to avoid hefty financial losses for the financial institution and its clients. A background in data science allows artificial intelligence professionals to train programs to walk this fine line by utilizing large, diverse, and accurate data sets.
In the fields of military and national defense, data science is perhaps even more vital to AI cybersecurity measures. To protect the nation’s resources, artificial intelligence developers don’t just train security programs to recognize and stop attacks — they engineer them to protect themselves.
Lt. Gen. Michael S. Groen, director of the United States Joint Artificial Intelligence Center, says engineering the nation’s cybersecurity AIs to guard themselves against threats is of the utmost importance.
“There’s special attention being paid to AI security within the [Department of Defense], so a lot of work is being done on testing vulnerabilities of algorithms and keeping a lid on spoofings, interruptions, and poisonings.”— Lt. General Michael S. Groen
His reference to data poisoning, the practice of hackers using belligerent AIs to corrupt the data AIs use to carry out tasks, demonstrates how a firm grasp of data science is essential for AI cybersecurity experts. They must train AIs with carefully-curated data and give AIs the means to protect it. Failing that, even the most advanced cybersecurity AIs fall apart.
What Does an AI Engineer Do?
AI specialists construct complex computer systems that help businesses or organizations carry out a wide range of duties. The exact responsibilities of AI technicians change with the industry, but they carry out similar tasks across all fields.
No matter the field, though, artificial intelligence technicians are responsible for four core actions:
- Building AI-driven programs to mine data, recognize relationships between that data, and solve key business problems. To accomplish that, artificial intelligence experts work with business intelligence analysts and have a strong understanding of the field to focus on the right data.
- Training programs to carry out tasks with reliable accuracy. An AI architect gives an AI-driven program a large set of test data and monitors its response. When issues arise, the engineer tunes a program’s AI system to produce the desired results.
- Validating how programs respond to and use data. After the training phase, AI technicians introduce a program to a new set of data to ensure it responds appropriately and actually learns from every action and data set. This is how engineers turn AI into programs that think for themselves.
- Integrating AI into a business’s daily operations. Once an AI program has been tested and validated, the artificial intelligence engineer works with different teams to make sure users can improve their daily duties with the program. This might include creating a user-friendly API, or application programming interface, and combining different forms of AI.
When these duties are completed successfully, the impact can be palpable for all stakeholders on a given project. For artificial intelligence programmers who create software for internal company use, their work can increase productivity, decrease downtime, and create valuable data that guides company policy. In that sense, companies can rely on AI experts to streamline daily operations and improve performance-related processes.
Some artificial intelligence professionals work on customer-facing software including:
- Navigation software
- Image recognition programs
- Translation software
- Virtual assistants
- Targeted, interest-based ad campaigns
- Facial recognition and security authentication software
- Smart home programs
In the case of chatbots and ad campaigns, artificial intelligence designers help marketing teams find the right audiences, appeal to that audience’s interests and promote products in a highly targeted way across multiple channels.
An AI designer’s work isn’t just used for marketing and customer service, however. An AI developer’s contribution can prove vital to the product or service a business is pushing. While the product development team designs a tangible, moving creation, the artificial intelligence team works on the products’ brains. They enliven it with the means to recognize and actually interact with its users.
Industries Requiring AI Engineers
AI knowledge is in-demand across an array of industries. As a result, AI programmers can find central roles, advancement opportunities, and leadership positions in any field that draws them
That flexibility and utility makes artificial intelligence a trending field across almost every industry:
- Marketing. Engineers create AI programs to collect consumer data, recognize trends, interact with customers, and present ads that audiences respond to.
- Public relations. In this field, artificial intelligence architects create programs that scan the web for hot topics and analyze the impressions events, products, and services garner in the press.
- Healthcare. AI programs aid every aspect of healthcare. Carefully trained AIs help with diagnosing diseases, creating patient care plans, organizing administrative data, scheduling appointments, and streamlining the patient experience.
- Military. With the help of AI, military intelligence officials can more quickly analyze satellite images, protect data and infrastructure, and conduct operations.
- Physical security. Security cameras and sensors use computer vision AI systems to alert users to potential threats and suspicious activity.
- Cybersecurity. AI designers train cybersecurity programs with vast amounts of data to identify and take countermeasures against malware, hackers, and suspicious behavior.
- Finance. Programs use artificial intelligence to identify and analyze data for making decisions about investing, responsible lending practices, spending, and credit scoring.
- Education. Though AI isn’t a replacement for human instruction, it helps educators reach students with interactive lessons, monitor student progress, and guide students with learning disabilities through otherwise troublesome tasks.
- Human resources. With proper training, AI programs help human resource managers recruit the right employees, tackle repetitive onboarding tasks, craft surveys for employee engagement, and more.
- E-commerce and retail. While AI marketing applications help virtual and brick-and-mortar businesses reach new audiences, they also conduct analysis essential to pricing, inventory management, online customer service, and supply chain management for physical and digital retailers.
In all of those industries, employers once had to budget vast amounts of time and money on menial yet integral tasks that proved time-consuming. With AI-driven programs, many of those tasks can now be automated in a user-friendly, accessible, and intelligent way.
That doesn’t mean AI is necessarily replacing workers. Instead, it makes their jobs easier and allows them to concentrate their efforts and talents in more rewarding, productive ways.
AI Engineers in Healthcare
How the healthcare industry uses AI is a great illustration of the technology’s life-enriching power.
When a patient logs onto their medical provider’s website, they can be greeted by a chatbot. In a conversational way, the bot schedules appointments, provides resources, runs insurance queries, and analyzes symptoms.
This was once the duty of a highly trained medical receptionist. This person would have to help patients on the phone, online, and in the waiting area. With the aid of an AI chatbot, the receptionist can focus on providing quality service to patients with more nuanced problems.
When patients have such problems, the chatbot can seamlessly connect them to real medical professionals. That means a more manageable workload for medical office staff and less hold time for patients.
Through rigorous testing and implementation, artificial intelligence architects make that possible. When trained by a skilled AI architect, chatbots learn how to respond to questions with different phrasing, translate different languages, and offer resources in the informed, comforting way an experienced professional would.
By 2026, experts foresee this particular application of AI chatbots to become a market worth $345.3 million.
AI Engineer Skills
Creating and maintaining artificial intelligence-driven programs requires a wide range of technical skills. To engineer AI programs and keep them working, artificial intelligence specialists use a combination of computer programming prowess and data science techniques.
To fill such a dynamic role, AI technicians require a number of IT and data science skills:
- Fluency in programming languages like Python, Java, and R
- Data mining and data analytics
- Using a combination of artificial intelligence tools like machine learning, algorithms, neural networks, and simulated annealing
- Applied mathematics
- Using logic and probability in IT and programming applications
- A multidisciplinary approach to data technology
AI Engineer Requirements
Artificial intelligence jobs require an in-depth working knowledge of programming and data science. Because of this, employers often seek applicants with a combination of formal education and real-world experience.
To start a career in AI, a person may need to meet certain education, certification, and experience requirements:
- A bachelor’s degree in data science, computer science, programming, or other technology-focused fields
- A technical knowledge base that covers multiple programming languages
- Experience working with machine learning, deep learning, and other AI systems and training techniques
For a more advanced position in the field, prospective AI developers should consider earning a Master’s degree in data science, data analytics, machine learning, artificial intelligence, or another field that combines technology with algorithms and logic. By earning an advanced degree, professionals in the industry can leverage their training and credentials to move into leadership roles.
Despite these requirements, artificial intelligence engineering is a broad term that encompasses professionals with a variety of skill sets. It’s a field that rewards specializing early in a career and broadening that focus over time.
AI Engineer Job Description
Because artificial intelligence is such a vast, diverse field, professionals in the industry carry a number of job titles. Employers often advertise these jobs under a large umbrella of related terms.
Some of the most common titles for AI industry professionals include:
- Intelligence designer
- AI expert
- AI application engineer
- Engineer or expert in a specific field of artificial intelligence (i.e. machine learning engineer or machine learning expert)
Though the responsibilities that come with each title differ slightly, job descriptions for artificial intelligence scientists and engineers cover some common tasks:
- Working closely with programming, research, marketing, user experience design, and logistics teams
- Conducting data research
- Staying informed about new AI technologies and methods
- Performing routine maintenance and testing
- Implementing AI technology to existing programs
- Being both creative and methodical in the implementation of AI technology
People starting a career in AI should also keep an eye out for job descriptions that include specific types of AI, user experience, data science, and business intelligence. While those terms don’t necessarily apply to jobs in artificial intelligence, they are often used to describe tasks and teams surrounding the implementation of AI.
Entry-level Artificial Intelligence Jobs
Because a computer automation specialist is expected to be well-versed in just about every aspect of artificial intelligence, someone looking for an entry-level artificial intelligence job should focus on specific roles in the industry:
- Junior data scientist. People in this role are responsible for finding and maintaining data. They may help an AI expert expand existing data sets, interpret data before it’s introduced to an AI system, and test AI with different kinds of data.
- Data analyst. Though data analysts and junior data scientists share some responsibilities, analysts are often in charge of reviewing data and presenting it to co-workers and other stakeholders. This analysis and communication is vital to ensuring an AI program has meaningful data to use.
- AI researcher. Instead of actually constructing artificial intelligence programs, AI researchers examine how different systems are used in their given industries. Armed with this preliminary research, AI architects can build new, innovative systems on existing artificial intelligence frameworks.
- Programmer and developer roles. Programmers write code for all kinds of computer software, but they don’t always work in AI. However, because AI systems are often built on simpler programs, understanding what goes into creating and maintaining those systems may prove valuable to one’s future career in artificial intelligence.
Each one of these roles plays an integral part in developing artificial intelligence technology. With these careers, future artificial intelligence professionals get hands-on experience with the pillars that support the industry as a whole. As a person advances their career in artificial intelligence engineering, their intimate working knowledge of these roles will be invaluable and highly marketable.
AI Engineer Salary
Today’s computer-reliant economy needs information technology and data science specialists. Because artificial intelligence experts combine those two disciplines, they are often in the position to demand relatively high salaries.
According to Payscale.com, people with technical AI skills earn an average salary of $125,000 per year. This may fluctuate with location, experience, and an AI scientist’s specific role and responsibilities.
People with the title of AI specialist, a very high-level position, earn an average of $132,995 per year. However, salaries within the first four years of this career are typically around $108,000, with some reaching salaries of up to $137,000 within five to nine years.
AI Engineer Job Growth
Because artificial intelligence professionals can work in such a broad selection of industries under different titles, it’s difficult to present the growth of AI technology as an industry itself in one statistic. Statista reports that in 2021, however, revenue earned from AI software totaled $34.87 billion. By 2025, their industry research team expects that to grow to $126 billion.
Overall, the Bureau of Labor Statistics expects computer and information technology occupations to grow by 13% from 2020 to 2030. For entry-level artificial intelligence programmers in data science, programming, and other roles, these positive growth rates indicate plenty of opportunities for professional growth.
How To Become an Artificial Intelligence Engineer in Four Steps
The versatility of an education in artificial intelligence works to benefit anyone entering the industry. To become a senior-level AI technician, there are countless paths one may take. At the same time, most conventional entries into the industry tend to follow a similar four-step route.
- Step 1: Get a Bachelor’s Degree
- Step 2: Seek Additional Certifications and Fluencies
- Step 3: Search for AI Engineer Jobs
- Step 4: Pursue a Master’s in Data Science
Step 1: Get a Bachelor’s Degree in IT, Computer Science, Data Science or Related Field
Future AI professionals can pursue bachelor’s degrees in data science, computer science, or information technology. This education gives students a broad understanding of the programming and data logic principles needed for further advancement.
Because algorithms and statistics play an important role in subfields of AI such as machine learning, a bachelor’s degree in mathematics may also be a great foundation. A minor in data science or computer science would make a great complement to this track.
Robotics and other tech-adjacent applied science degrees also serve as a great basis for a future career in artificial intelligence.
Step 2: Seek Additional Certifications and Fluencies
Programs and majors that don’t involve a heavy programming curriculum can be supplemented with programming language certification courses. This will build the technical expertise required to become a lead artificial intelligence technician.
For people who haven’t acquired a data science major or minor, going through a data science certification program can help them develop the skills and resume needed for many artificial intelligence jobs and academic programs. While these kinds of certificate programs may not include the exhaustive and broad approach that many degree programs offer, they feature a laser-focused curriculum that introduces students to the field in an expedited way.
Step 3: Search for AI Engineer Jobs
Additionally, entry-level AI jobs may not involve working directly with AI programs. However, securing a job as a programmer, data researcher, or junior software engineer may mean working with a team working on a larger AI-driven project. This could be a valuable opportunity to learn how different disciplines come together to make artificial intelligence possible.
Depending on the industry, a combination of education, certifications, and hands-on experience (and potentially on-the-job training) makes it possible to move into an AI architecture role.
Step 4: Pursue a Master’s in Data Science
A master’s degree in data science may open the door to more senior-level AI engineer jobs. It’s an interdisciplinary field that combines computer science and mathematical principles with real-world data analysis and structuring skills — the cornerstones of an artificial intelligence professional’s job.
With this degree, intermediate AI programmers may become experts in troubleshooting issues with AI training data, sourcing the right kinds of data, and using that information to meet a company or organization’s goals. This deeper understanding of how AIs use data allows for more creative implementation of data analytics and multiple types of artificial intelligence.
Some programs, like the data science Master’s program offered at the University of Southern California, integrate artificial intelligence courses into the curriculum. However, broader data science master’s programs include data theory principles applicable to every career in AI.
Start a Career in Artificial Intelligence Engineering
Artificial intelligence has transformed from a science fiction trope into a real-world tool that shapes the human experience. It’s used in almost every industry and advances at breakneck speeds, so there is a huge demand for experts in this cutting-edge field. For technology-minded data science experts, there has never been a better time to start a career in AI.
To find programs featuring courses in machine learning, neural networks, data science, and other exciting subfields of AI, request more information and explore how to become an AI architect in a world increasingly molded by automated computer systems.