Important points
Artificial intelligence (AI) engineers leverage knowledge of AI and machine learning to develop computer applications and systems.
-
AI engineering has many applications, including recommender systems for streaming platforms like Netflix, self-driving cars, and surgical robots.
-
To start a career in AI engineering, start by learning technical skills like programming and statistics, apply for entry-level jobs, and gain work experience in the field.
Learn more about how AI is impacting the world In this lecture from DeepLearning.AI’s AI ForEveryone course:
What is AI engineering?
Artificial intelligence engineers are individuals who use AI and machine learning techniques to develop applications and systems that can help organizations improve efficiency, reduce costs, increase profits, and make better business decisions.
According to an IDC report, AI could contribute up to $19.9 trillion to the global economy by 2030, increasing global GDP by 3.5%. [2]. This projected growth means that organizations are turning to AI to enhance business decision-making and improve efficiency.
AI engineering focuses on developing tools, systems, and processes that enable the application of artificial intelligence to the real world. Any application where a machine imitates human functions, such as problem solving or learning, can be considered artificial intelligence. Algorithms are “trained” by data, which helps them learn and improve performance.
Everyday examples of AI engineering
While using Netflix, Spotify, or YouTube, you may have encountered the result of AI engineering, where machine learning customizes suggestions based on your behavior. Another popular example is transportation, where self-driving cars are powered by AI and machine learning technology. AI is particularly useful in the healthcare industry, as it allows robots to perform surgeries and generate automated diagnostic imaging.
Learn more about how AI will impact our world with this lecture from DeepLearning.AI’s AI ForEveryone course.
Responsibilities of an AI Engineer: What does an AI Engineer formally do?
AI engineers play a critical role in organizations using AI. They illustrate the AI strategy and define the problem that AI should solve. They are responsible for building and implementing AI development and production infrastructure. The specific tasks and responsibilities of an AI engineer include:
AI Engineer Salary and Job Outlook
AI engineering is a specialty with good job growth and tends to have high salaries.
As of October 2025, the median gross salary for an AI engineer in the United States is $138,000, according to Glassdoor. [1]. This figure includes base salary and additional pay, which may represent profit sharing, commissions, bonuses, or other compensation. According to the U.S. Bureau of Labor Statistics (BLS), the average salary for AI engineers who fall into the Computer and Information Research Scientist category is $140,910. [3].
Employment is projected to grow by 20% between 2024 and 2034, much faster than the average for all occupations. [3]. According to Veritone’s Q1 2025 job market analysis, AI/ML engineer jobs are one of the fastest growing AI jobs, with a 41.8% year-over-year increase. [4].
AI engineers typically work for technology companies like Google, IBM, and Meta, helping them improve products, software, operations, and delivery. It is also likely that government agencies and research facilities seeking to improve public services will increasingly hire them.
How to become an AI engineer
Becoming an AI engineer requires basic computer, information technology (IT), and math skills. These are important for operating artificial intelligence programs. Here’s how to get started with AI engineering.
1. Learn the skills you need.
You will need to build technical skills, including knowledge of the tools that AI engineers typically use.
-
programming: You may want to learn a programming language such as Python, R, Java, or C++ so you can build and implement models.
-
Probability, statistics, and linear algebra: These are required to implement various AI and machine learning models.
-
Big data technology: AI engineers work with large amounts of data, so they need knowledge of Apache Spark, Hadoop, and MongoDB to manage it all.
-
Algorithms and frameworks: You should understand machine learning algorithms such as linear regression and naive Bayes, and deep learning algorithms such as recurrent neural networks and generative adversarial networks, and be able to implement them using the framework. Popular AI frameworks include Theano, TensorFlow, Caffe, Keras, and PyTorch.
These skills can be learned through online courses or boot camps specifically designed to start a career in the field of artificial intelligence.
Not sure where to start? Consider enrolling in the University of Michigan’s Python for Everyone Specialization and learn how to program and analyze data using Python in just two months. Meanwhile, for the basics of machine learning, check out Stanford University and DeepLearning.AI’s machine learning specialization.
Do I need a degree to become an AI engineer?
No, you don’t necessarily need a degree to become an AI engineer. More and more people are turning to professional certification programs to learn necessary skills and prepare for interviews.
Still, many companies require at least a bachelor’s degree for entry-level jobs. Common majors include computer science and information technology. Jobs in the AI field are highly competitive, but if you can prove you have the right skills and successfully interview, you can start your career as an AI engineer.
2. Apply for a job.
If you have little or no experience in a field, applying for a job can be intimidating. However, it may be helpful to know that people are hired every day for jobs they have no experience in. There are many ways to enhance your resume for success in your job search. For AI engineering jobs, you may want to highlight specific projects you worked on in your work or classes that demonstrate your broad understanding of AI engineering.
If you apply for a job, you will probably be asked for an interview. Don’t be discouraged if you apply to dozens of jobs and don’t hear back. Data science is typically a highly sought-after (and lucrative) career field, so companies may receive hundreds of applications for a single job.
Preparing for an interview requires practice and preparation, especially for technical roles such as AI engineer. You need to hone your interviewing skills so you can prove to the hiring manager that you’re the best fit for the job. You will be asked to explain why you develop, deploy, and scale a particular algorithm. These interviews can be very technical, so make sure you can clearly explain how you solved the problem and why you chose to solve it that way.
read more: Job Guide: Resources for your next career move
3. Gain work experience.
Even for entry-level positions and internships, there may be several interviews. But once you find a job, it’s time to prove yourself and learn as much as possible. You’ll be able to apply the skills you learn to deliver business insights and solutions that can change people’s lives, including in healthcare, entertainment, transportation, and consumer product manufacturing.
Prepare for a career in AI engineering with Coursera
Ready to keep exploring? Find courses, career paths, skills, and more with practical guides in our Career Resource Hub. Or, if you want to learn more about AI careers, courses, and skills, check out our free resources.
Whether you want to develop new skills, become familiar with in-demand technology, or improve your abilities, keep growing with a Coursera Plus subscription. Access over 10,000 flexible courses.
