How to Become an AI Engineer: Courses, Skills, and Certifications You Need
So you want to become an AI engineer…
The good news is that AI is one of the most exciting and future-proof moves you can make. Companies across all kinds of industries, from healthcare to tech, are racing to adopt artificial intelligence…but they’re missing one vital piece:
Skilled professionals who can build, deploy, and maintain these systems.
If you’re wondering how to get started in a lucrative career in AI, this guide will walk you through everything you need to know. From the skills, courses, and certifications that can help you break into AI to flexible course options like those offered by ONLC.
What is an AI Engineer?
While we hope you already know what an AI engineer is, if you need a bit more of a technical answer, here it is.
An AI Engineer is a technology professional who designs, develops, and deploys artificial intelligence systems that can perform tasks traditionally requiring human intelligence, such as decision-making, pattern recognition, natural language processing, and predictive analytics. Their role blends software engineering, data science, and machine learning expertise to create practical AI solutions for real-world applications.
Here’s the thing…
Unlike AI specialists, AI engineers not only build and train models using algorithms and large datasets but also handle the infrastructure needed to deploy and scale those models reliably.
And because AI touches everything from chatbots and recommendation systems to autonomous vehicles and medical diagnostics, AI engineers play a critical role in shaping how organizations harness data and automation to solve complex problems. With great power comes great responsibility (and soft skills).
So that leads us to our next section… what skills and mindsets do you really need to become an AI engineer?
The Skills and Mindset You Need
AI engineering blends computer science, data handling, and machine learning expertise with problem-solving and communication skills. While there isn’t one single “become an AI engineer” course you can take, a mix of several courses that cater to different skill sets will do you the best.
Foundational Skill Courses
First things first, you need the foundational skills…
Here’s where you can start: ONLC offers training like Python Programming Training (including “Python for Non-Programmers,” “Python for Data Science,” and advanced Python courses), which teach data types, control structures, functions, object-oriented programming, and real-world libraries such as NumPy and Pandas.
Mathematic Skill Courses
For the mathematics side of things (linear algebra, statistics, probability), ONLC’s Machine Learning & Artificial Intelligence courses cover foundational principles and algorithms, helping learners understand how models operate, why optimization matters, and how to evaluate performance.
Applied Learning Courses
Next, as you move into more applied work (cleaning, transforming, analyzing large datasets, or setting up data pipelines), you’ll benefit from ONLC’s programming classes and the Essential AI Workbench: Python + GitHub + Copilot X + VS Code.
Aspiring AI engineers love that particular course because it gives them hands-on experience setting up development environments, integrating tools, and editing or debugging AI-assisted code.
Model Building Courses
On the model-building side, ONLC’s offerings in Machine Learning & AI include training in practical tools and algorithms, while specialized courses such as “Creating Your Own Chat Agent / Using OpenAI API” or “Using Huggingface for Specialized Models and Fine-Tuning” teach you how to build models, adapt them, and fine-tune them for specific tasks.
Deployment, Infrastructure, and Cloud Work Courses
When it comes to deployment, infrastructure, and cloud work, courses like Azure AI Fundamentals (AI-900) and AI-102: Designing and Implementing an Azure AI Solution help with using cloud ML tools, cognitive services, and implementing AI in production environments.
Soft Skill Courses
Finally, for soft skills (communication, ethics, problem framing), ONLC offers generative AI courses like Introduction to Generative AI for Copilot Users, which not only teach technical usage of tools like Copilot, but also prompt engineering, understanding of large language models, and considerations of responsible or “ethical” AI usage.
These all help you work effectively in cross-functional settings and ensure that your AI solutions are not just technically correct but practical, explainable, and aligned with real business/organizational needs.
Can I learn AI concepts like computer vision and NLP at my own pace?
Yes. Many online platforms and training centers offer self-paced courses covering everything from foundational knowledge to advanced AI agents and deep learning strategies. Learning at your own pace allows you to fit training into your schedule while gradually building the depth of understanding needed to perform real-world AI tasks.
How does Python fit into the AI learning path?
Python is the most widely used programming language in AI because of its simple syntax, extensive libraries (like TensorFlow, PyTorch, and scikit-learn), and large developer community. Learning Python early provides a strong foundation for exploring machine learning models, AI applications, and deep learning frameworks.
Wondering how to tackle Python? Check out our post “How to Learn Python,” which gives you practical tips, and “Is Python Hard to Learn?” breaks down what makes Python accessible for beginners. These are great resources as you build your foundation.
Career Paths for AI Engineers
Let’s say you take all the courses and become accredited, the big question now is “what do I do with all these skills?”.
Artificial intelligence spans hundreds of industries and applications, each with pros and cons. While everyone is different, a lot of AI engineers specialize in high-impact areas like natural language processing, reinforcement learning, or deep learning, which lets them develop expertise in building advanced AI models and applications.
What industries are expected to benefit most from AI applications?
AI adoption is expanding across many sectors. In healthcare, AI models assist in diagnosing diseases and analyzing patient data. Or take transportation, for example, computer vision systems help with autonomous driving and traffic prediction. Other industries, like finance, retail, and manufacturing, also use AI to improve productivity, reduce costs, and deliver personalized experiences.
Common Challenges and How to Overcome Them
Nothing worth it is easy; the same applies to aspiring AI engineers. But don’t worry, after taking hundreds of professionals through career growth and transition, we have some insight for you.
The first issue is that many learners feel overwhelmed by the sheer amount of material available. The best solution here is to start small and focus on structured learning paths rather than jumping between random tutorials.
For example, ONLC’s instructor-led and on-demand options can help keep your studies organized and efficient. At their core, all of our programs are designed to help you develop these in-demand skills through targeted learning paths… which definitely beats learning through YouTube.
Another challenge is the lack of real-world project experience. While we do have courses that contribute to real-world applications, such as model building, the rest might be up to your own initiative. That being said, to get some real-world experience under your belt, build small but meaningful projects along the way, like creating a recommendation engine, analyzing text sentiment, or deploying a chatbot on the cloud.
Why? Employers value portfolios that prove you can apply concepts, not just talk about them…
Finally, AI evolves rapidly, so staying current is truly critical. Be sure to follow AI news, join online communities, and experiment with new tools and techniques to stay ahead.
What learning strategies help people stay committed to AI training programs?
Taking it slow and breaking your learning path into milestones helps you stay on track. To do this, start with Python and basic machine learning, then move to neural networks, NLP, and computer vision.
On top of that, setting a schedule, practicing with real datasets, and completing small projects along the way will keep you motivated while building practical experience for certification exams and future job roles.
How do I discover the right AI learning path for my goals?
Your ideal path depends on whether you want to specialize in machine learning, natural language processing, computer vision, or AI infrastructure.
Beginners often start with foundational Python and machine learning courses, while more advanced learners dive into deep learning, AI agents, or specific applications like healthcare or transportation. Many training providers offer guided paths so you can learn step-by-step rather than feeling overwhelmed.
How ONLC Fits into the Journey
ONLC offers live, instructor-led training combined with on-demand flexibility, making it ideal for professionals balancing learning with work. Our catalog covers programming, data analytics, AI fundamentals, and certification prep for platforms like AWS and Azure, all essential for AI engineers.
We recommend starting with self-paced introductory courses and then moving into live classes for complex topics like machine learning or cloud deployment. In addition to our world-class courses, our certification prep options ensure you’re ready for exams that carry weight in the job market, while custom training paths can support teams or organizations building AI capabilities together.
In short, year over year, we’ve been helping learners qualify for jobs in AI engineering and other applications, changing the entire trajectory of their careers and what they thought was possible.
If you think you’re ready for the next step, contact one of our experts today.
Key Takeaways
You must build strong foundations in programming, math, and managing data before venturing into machine learning, deep learning, and deployment capabilities.
While it may be a convoluted, long journey, ONLC’s cutting-edge flexible training enables you to progress from beginner to job-ready at your own pace, with instructor-guided instruction and home-based convenience. With an easy-to-follow game plan, consistent practice, and a portfolio of actual work, you can get started in the fast-growing profession of AI engineering confidently.
