How to Become an AI Engineer in 4 Easy, Reliable Steps
All anyone talks about is the power of AI. Companies like Microsoft, Nvidia, and Meta have finance professionals foaming at AI stock prices and the workforce thinking about a career change into the booming field of AI engineering.
LinkedIn reported that between July 2021 and July 2023, job postings mentioning AI more than doubled. The monthly Tech Jobs Report from CompTia tracks that 42% (17,479) of recent tech job postings (40,947) are AI-related jobs or cite AI skills. But how can you take advantage of this growing field and break out into a successful career as an AI engineer?
You can keep reading! Let’s break down what an AI engineer is, what they do, and the steps you can take to become one.
Table of Contents
- What is an AI Engineer?
- What Are the Responsibilities of an AI Engineer?
- How to Become an AI Engineer
- Start Building Your Career as an “AI Engineer”
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What is an AI Engineer?
AI engineers use machine learning, data science, and neural networks to develop and train computers to think and act like humans. They manipulate and maintain large data sets to develop, test, and fix machine learning models and applications.
While pop culture would have us think that software developers and engineers are loners who click clack away on a computer while sitting in a dark room, that couldn’t be further from the truth! AI engineering is a highly collaborative field where you regularly work with team members and stakeholders to strategize, design, build, review, and test AI applications. The job requires extensive skills in programming languages, machine learning algorithms, and big data. And these profitable skills directly translate to a profitable career.
The average AI engineer salary is $155,000, but some companies can afford to pay their employees well over half a million dollars! When you dive into the responsibilities and technical skills required to be an AI engineer, it makes sense why they’re some of the highest-paid tech professionals.
What Are the Responsibilities of an AI Engineer?
Consider AI engineer an umbrella term for professionals in AI. In the same way that “athlete” could describe basketball, football, and baseball players, you could call many AI professionals an “AI engineer,” even when it’s not their official title. Depending on the industry and the job position, the responsibilities of an AI engineer will vary, but there are a few day-to-day responsibilities every AI engineer can expect to do.
In your AI engineer career, your responsibilities might include:
- Creating and maintaining AI development processes
- Building and testing AI models and software from large data sets
- Developing machine learning algorithms and deploying them in applications
- Transforming machine learning models into application programming interfaces (APIs) so other apps can use them
- Analyzing and drawing insights from testing results to guide company decisions
- Collaborating with AI team members for software development, testing, and integration
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How to Become an AI Engineer
Becoming an AI engineer can be challenging, but the process doesn’t have to be complicated. While it might take some time to break into the industry, you can do it in four easy-to-follow steps.
1. Learn the Required Skills
There are technical and non-technical skills every AI engineer should have to transition into any career path. The non-technical skills, or soft skills, will change based on the exact position, but there are several must-have AI skills in demand everywhere. The top skill sets for AI engineers are:
- Programming languages (Python, Java, JavaScript, C++, R, Lisp, Haskell, Julia, Prolog, Scala)
- Machine learning frameworks (TensorFlow, PyTorch, Keras)
- Mathematical skills (Linear algebra, linear regression, statistics)
- Big data technologies (Apache Hadoop and Apache Spark)
- API development tools (Makefile, MySQL, XML)
- Data science (Data visualization, data mining, data wrangling)
- Neural networks principles and architectures
- Non-technical skills (Critical thinking, problem-solving, emotional intelligence, communication)
These skills may look like they require formal education, but that’s not always the case with AI systems — or the tech industry as a whole. You can learn AI engineering skills inside or outside of a traditional classroom. Three methods for learning the required skills for AI engineers include earning a college degree, enrolling in an AI course, or earning a professional certification — or any combination of the three.
Earn a College Degree
Most colleges in the United States don’t offer Bachelor’s degrees in artificial intelligence. AI is a growing industry, and that’s reflected as more and more colleges and universities add AI degrees or courses to their curriculum.
To make sure you’re on the right track, you’d have to sign up for a Bachelor of Science (BS) in computer science or another related field. On top of your computer science courses, you’d need to tack on specific courses in AI, machine learning, and data science. And if you decide to take your formal education one step further, earning a Master’s degree in AI or machine learning is actually more accessible than finding a school that offers AI for undergraduates.
Pursuing a degree comes with a lot — a lot of skills, time, and tuition. Colleges like Carnegie Mellon and the Massachusetts Institute of Technology (MIT) are recognized for having two of the best undergraduate AI programs, but the tuition and fees are enough to stop any budding AI engineer in their tracks. Fortunately, higher education isn’t the only way to learn the required skills for a successful career as an artificial intelligence engineer.
Enroll in an AI Course
When you earn an Associate’s or Bachelor’s degree, some of your classes can feel pointless. Instead of focusing on your career, you find yourself taking random psychology or sociology courses just to meet credit requirements. If paying for college is out of your budget, skip paying for all the extra classes. There are tons of free (and paid) online AI courses you can take to dive straight into the skills you need — programming languages, deep learning, neural networks, and more.
To learn more about AI technology, check out these free AI courses from some of the most respected universities and tech companies.
- AI For Everyone (from DeepLearning.AI)
- Introduction to Artificial Intelligence (from IBM)
- Artificial Intelligence (from Columbia University)
- Intro to Python (from learnpython.org)
- Introduction to Programming with Python – Full University Course (from Harvard University)
- Practical Machine Learning (from Johns Hopkins University)
- Machine Learning Crash Course with TensorFlow APIs (from Google)
- Machine Learning with Python (from IBM)
- Intro to Deep Learning (from Kaggle)
- Convolutional Neural Networks for Visual Recognition (from Stanford University)
- Data Visualization with Python (from IBM)
- Speaking Of: Machine Translation and Natural Language Processing (from Amazon)
- Deep Learning for Natural Language Processing (from the University of Oxford)
Earn a Professional Certification
The ROI (return on investment) of earning a professional certification in AI is massive when you compare the time and money spent versus the benefits. Professional certifications can be pricey, but they can also increase your earning potential, boost your credibility, and give you an edge over other AI engineers. If it’s been a while since you’ve learned the basics of AI, they’re a great way to relearn and reinforce the skills you already have.
Similar to AI courses, there are several free and paid professional AI certifications that you can earn without ever leaving your home.
IBM AI Engineering Professional Certificate (from IBM)
IBM Applied AI Professional Certificate (from IBM)
Google Data Analytics Certificate (from Google)
Stanford Professional Certificate in Artificial Intelligence (from Stanford University)
Intel® Edge AI Certification (from Intel®)
Applied Machine Learning and AI (from Cornell University)
Microsoft Certified: Azure AI Engineer Associate (from Microsoft)
Certified Artificial Intelligence Scientist (from United States Artificial Intelligence Institute)
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2. Get Experience
Your AI knowledge and skills are one of the most important factors in becoming an AI engineer. Building a history of experience comes next. A lot of entry-level tech jobs ask for 2-3 years of experience. You’re then caught in an endless loop of needing experience to get experience. Luckily, dozens of entry-level tech jobs require no experience. But if your lack of experience can’t get you in the door of your dream job, you can still get experience in tech without biting the bullet of an unpaid internship or project.
After learning your required skills, put them to work by:
- Freelancing
- Developing your own AI projects
- Contributing to open-source projects
- Participating in hackathons
- Producing recreated or mock work
3. Network with the AI Community
You don’t have to network your way into a career as an AI engineer, but it doesn’t hurt to have a group of peers and mentors in your corner. Networking gives you access to professional development and career opportunities that you might not know exist. In fact, Matt Youngquist, the president of Career Horizons — a career coaching company — claims that most jobs are not published, estimating that 70-80% of jobs are filled through personal and professional connections. You need to get ahead of the curve and join an AI community.
Active AI communities seem scarce, but OpenAI Community and Global AI Community are two companies with thriving resources, member participation, and community events.
4. Apply for Jobs
At this stage, you could easily call yourself an AI engineer, but landing a job can help you build confidence in yourself and your skills. It’ll also help you get that professional experience that so many employers are looking for.
LinkedIn, Indeed, or a simple Google search are great places to find AI engineer openings. Your resume and portfolio might be enough to get you through the door, but knowing how to answer technical interview questions will keep you in the building.
Is Tech Right For you? Take Our 3-Minute Quiz!
You Will Learn: If a career in tech is right for you What tech careers fit your strengths What skills you need to reach your goals
Start Building Your Career as an “AI Engineer”
AI engineering is a broad term for the work AI professionals do. In fact, you’re likely to see fewer companies looking for an “AI engineer” and more companies with job listings in specified areas like research, data science, and machine learning. So don’t limit yourself to one “AI engineering jobs” search.
While its domination in the tech industry is a given, AI is taking over in the healthcare, finance, and retail industries. And AI engineers have their pick of these companies as they put out searches for:
- AI Research Scientists
- AI Developer
- Machine Learning Engineers
- Data Scientists
- Data Engineers
- Natural Language Processing Engineers
- Software Developers
- AI Data Analysts
- Deep Learning Engineer
- Robotics Engineer
The name of the position might change, but the skills are guaranteed — for the most part — to stay the same. And if you’re not ready to dive into the deep end of a career as an AI engineer, it’s okay to get your feet wet. The Skillcrush Break Into Tech program is a good opportunity to get a feel for the basics of AI and more information on the job outlook. The online course teaches Python — one of the most desired AI programming skills — before giving you all the career counseling you’ll need to get hired and build a successful career in AI.
