If you’re here, chances are you want to learn how to become an AI engineer.
Maybe you’ve played with prompts. Maybe you’ve watched one too many reels about ChatGPT stealing jobs. And maybe you’ve been quietly stacking skills on nights and weekends while everyone else scrolls. But at the same time, you’re not exactly sure where to start.
And while you know you’re not another prompt chaser or headline skimmer, you also know it’s time to pivot from those years of building a career inside other people’s systems.
Well, here’s the good news: you can start where you are, no matter your educational background or domain. Yep, that’s the beauty of the no-code era we’re in.
As Mehreen Omer, Mindvalley’s product marketing manager and resident AI expert, puts it, “Anyone can be an AI engineer if they know how to build with AI.”
What is an AI engineer?
An AI engineer builds smart systems that solve real-world problems. They design workflows, tools, and automations using AI fluency, or the skill of knowing what to build, which model to use, and how to train it to think in context.
But Mehreen, who’s also the co-founder of Crazy Agents, a generative AI startup building autonomous agents with narrative intelligence, has a more fun answer for you.
“It’s someone who simply has imagination to build stuff,” she says in an exclusive sit-down with Pulse. “They’re not just passive users, but proactive builders.” Or quick learners, in short.
And there’s a huge demand for people with this edge. According to a PwC report, by 2030, AI is estimated to contribute up to $15.7 trillion to the global economy. (For perspective, that’s more than China and India’s combined output.) Ultimately, this growth means companies everywhere are turning to AI to boost their business decisions and increase efficiency.
If you’ve been wondering whether AI engineering requires a computer science degree, the short answer is: you probably don’t. Here’s why: university education requirements for AI roles have declined by 15%, according to a 2023 study. As the report further notes, AI skills now command a 23% wage premium, often surpassing the value of degrees and PhDs.
So, if you already know how to spot problems, design processes, or move fast inside a business? Newsflash: you’ve already got half the blueprint.
All you need to do, from this point onwards, is get going on how to learn AI. It’s the surefire path to expediting your professional timeline and helping businesses expedite theirs.
Anyone can be an AI engineer if they know how to build with AI.
— Mehreen Omer, product marketing manager at Mindvalley
What does an AI engineer do?
AI engineers rely on generative AI platforms like OpenAI’s ChatGPT, application programming interfaces (APIs), and automation tools to create workflows that learn, adapt, and scale. This trifecta is what makes AI for business so powerful today.
Sometimes, this means training machine learning models. At other times, it’s building a GPT that knows how to respond to your customers. In between, there are also tasks like writing internal SOPs or organizing product feedback.
Other common use cases that could add to the AI engineer job description include:
- Creating AI agents that handle customer support,
- Automating analytics dashboards with real-time data,
- Tagging and routing incoming leads from multiple channels,
- Fine-tuning models on internal data for personalization, and
- Setting up no-code tools that trigger decisions based on user behavior.
All in the name of building systems that think ahead, so you don’t have to look back. And this optimization matters more than you think. Think about launching your products or services faster, serving your clients better, and freeing up time for your skill-stacking pursuits.
This kind of high-level overview, for one, is how Vykintas Glodenis, Mindvalley’s very own chief AI transformation officer, gets results.
“Think in terms of one three-step workflow you’re doing on a daily basis,” he advises in Amplify with AI, Mindvalley’s new program on all things AI engineering. “If you improve your work just by 3% weekly, you’ll be 4.5x more productive by year’s end.”
How to become an AI engineer in 5 steps
Thankfully, you don’t need to master Python or collect certificates to start. What you do need is a problem to solve, a project to build, and a system that improves every week.
“AI engineering isn’t about learning everything at once,” says Mehreen. “It’s about learning just enough to build something. The more you build, the more your skills catch up.”
So if you’re ready to stop dabbling and start elevating your game from right where you are, career-wise, here’s how:
1. Shift your mindset
Scroll LinkedIn at any time of the day, and you’ll see panicky sentiments everywhere. Employees are ranting nonstop about AI taking over jobs. Founders are asking if they need to “pause hiring,” and entire teams of creatives, analysts, and project managers are sounding off on how they’ll fit in (or not) in this new wave.
But here’s what most people miss: AI doesn’t take your value away. It gives you a way to level it up. Here are some examples:
- The designer who learns to automate can start thinking like a creative director.
- The content writer who understands AI workflows now becomes a strategist.
- The remote-working virtual assistant with smart systems set up can now explore the operations side of things more easily.
- Even the accountant who trains a finance bot can start acting like a product lead.
These are just some examples of what thinking one level higher looks like, showing that everything, in the end, is about thinking in systems and understanding how to manage workflows—a.k.a. the beating heart of AI engineering.
No wonder Mehreen is quick to remind you that, despite the myth, the path isn’t for software engineers only. AI engineering, as she points out, is “for anyone who wants to build with imagination.”
All you need to do is stop overthinking, and, as she adds, “to be excited, not overwhelmed. Once you get curious about what’s possible, you start thinking like a builder.”
2. Master your tools like muscle memory
You don’t need a 20-tab tech stack to start building the right workflows.
What you do need is one you know by heart and can ultimately reach for without second-guessing. Because how can you master one if you’re busy chasing the hyped up apps? As Vykintas points out, “You want to build a relationship with your stack the way a coder builds muscle memory with their keyboard.”
Start with a few of these:
- ChatGPT: For thinking, drafting, solving, and building in context
- Notion AI: Your control tower for managing, tracking, and documenting
Zapier: The connecting platform for no-code workflows that sync all of your systems across other apps. - Make: For logic-based automations that go deeper than Zapier can.
- Suno: To generate custom music and audio (for podcasters, creators, and brands).
- Midjourney: To generate visual assets, illustrations, and brand kits (and master how to make money with AI art).
These tools become powerful once they’re part of your operating system. So instead of asking “What tool should I use?”, ask yourself, “What task do I do every week that takes too much time?”
From there:
- Map the workflow steps you need to take.
- Use one tool to automate them.
- Run the workflow for a week to see what happens.
From here, it’s easier to build the next loop. And once your AI stack works like muscle memory, your focus shifts from tasks to entire systems. And that’s where the real purpose of AI engineering truly shines.
3. Pick a real-world problem to solve
You don’t become an AI engineer by picking the perfect course. You become one by solving a problem that matters to you, or to people willing to pay for it (and so many do, just to save time).
So, you can skip those tutorials and look at your life, your job, and your clientele instead. What’s one thing in your current work that’s clunky, manual, or too repetitive to scale? That’s your entry point to building your first AI-driven use case.
“I’m a big believer in the one problem idea,” says Mehreen. “Focus on one mini problem you’re planning to solve, and just build on that. Don’t overcomplicate it.”
A few real-world examples of different roles using AI to simplify their lives:
- A podcast editor automates episode descriptions and timestamps.
- A lifestyle coach turns intake forms into personalized onboarding sequences.
- A project manager builds a dashboard that updates team status reports in real time.
- A sales rep creates a GPT that writes follow-ups in their tone of voice.
Once your problem is solved? You can move straight to the next one, using the time you’ve freed up with AI to think bigger, faster, and more creatively.
Even Vishen, the founder and CEO of Mindvalley, has leveraged the AI revolution. “It used to take us eight hours to do one of these sales presentations,” he shares in Amplify with AI. “Now it’s 30 minutes… and with every event, it gets smarter and smarter.”
The result? A full-scale-up, unlocked. “When we launched [AI Mastery] in August 2023, it rapidly became the best-selling program in the 20-year history of Mindvalley,” he says. “It blew us away because of how powerful AI is at saving time.”
4. Build workflows that scale, not just outputs
“If you want to learn AI, apply it,” says Mehreen. “Build something simple that gets used. That’s how your skill grows.” Yep, “used” being the keyword here.
Because once something’s useful, you’ve got a real case for product utility, which is the whole point of AI engineering in the first place. You don’t just build interconnected systems for the sake of it; they’re all there, linked up seamlessly, to solve an end user’s pain point.
Not sure where to begin? Below are some exemplary foundations of a working AI system, each one tied to a single problem, powered by relevant datasets, and designed to run with minimal input:
- A Notion-based podcast pipeline that autogenerates show notes,
- A custom GPT trained on client FAQs that drafts support replies,
- A dashboard that pulls analytics and summarizes them weekly, or
- A Make automation that sends lead follow-ups, tailored by the right user persona.
Each one works because it’s powered by the right information, organized clearly, and built to drive a decision or output. All without you having to lift a finger.
Should you feel overwhelmed by it all, follow Vykintas’ rule of thumb: “Don’t overarchitect the system. Build one working loop. Then, stack [over time].”
5. Iterate your solutions weekly
If you want to become an AI engineer, don’t wait to “get good.” Ship something this week. Watch how it runs, break it, then improve it.
This path rewards people who move, not those who overplan. Think of your AI-powered systems as living feedback loops. What you build today teaches you what to refine tomorrow.
Vykintas emphasizes that your success boils down to your keenness to be a quick learner. As he points out, “Ask [yourself] every week: What can I improve with AI next?”
Mehreen, for one, lives by this ethos herself. She used to spend hours rewriting the same outreach emails and pitch decks… that is, until she built a simple system. “Now, I just update the core Notion page,” she says, “and everything else runs off it. Custom intros, reminders, even next steps. That one shift probably saves me three hours a week.”
Well, like Vykintas and Mehreen, you, too, can embrace the art of consistency from where you are. It’s ultimately how skill-stacking in the age of automation works… and how your career wins compound over time. Week by week, one feedback loop at a time.
What are some of the AI engineering certifications you can take?
You won’t build an AI engineering career off one course. But the right one gets your hands dirty and helps you position your work to be seen.
Look for programs that get you building with real tools and workflows that sharpen your edge in your niche. Bonus points if it’s taught by someone who’s built a working system, not just written about one.
Here are some good options to try, if you don’t know which to go with:
- Microsoft Certified: Azure AI Engineer Associate. Learn to build and deploy AI solutions using Azure Cognitive Services, machine learning, and AI agents. Ideal if you’re working in Microsoft’s cloud ecosystem.
- IBM AI Engineering Professional Certificate (on Coursera). A technical path for coders or analysts that covers Python, machine learning, and deep learning with hands-on labs and real-world datasets.
- Google AI Essentials. A great entry point for non-tech professionals. Offers foundational knowledge and practical skills around responsible AI use and applied tools.
- AWS Certified Machine Learning—Specialty: Perfect if you’re building solutions on Amazon Web Services. This cert proves you can train, tune, and deploy ML models in production.
- NVIDIA Deep Learning Institute Certifications. It’s a high-demand credential in industries like robotics, autonomous vehicles, and healthcare, especially if you’re working in GPU-accelerated environments.
- Certified Artificial Intelligence Engineer (CAIE™). Offered by the U.S. Artificial Intelligence Institute, this cert focuses on system training, dataset selection, and model testing.
- AiE™ (Artificial Intelligence Engineer by ARTiBA). A vendor-neutral certification that signals practical AI design skills for cross-industry applications. It’s especially useful if you’re applying outside the Big Tech bubble.
Whichever you choose, anchor it in the purpose of why you’re pursuing it to begin with. As Mehreen puts it, “Don’t pick certifications for the badge. Pick the ones that make you build.”
What is an AI engineer’s salary?
According to Glassdoor, AI engineers in the U.S. typically start with base salaries between $84,000 and $136,000. But factor in additional payments (like allowances), annual bonuses and equity, and the total compensation can significantly increase.
But hedge funds are pushing the numbers higher. For instance, Point72, a major global hedge fund, has advertised AI engineer roles with base salaries up to $400,000, plus performance-based bonuses. These roles often involve collaborating with data scientists, engineers, product teams, and compliance experts.
In short? AI engineers can make a lot of money, even without a software-related background.
Being an industry insider, Mehreen has witnessed this firsthand. “People think you need to be a machine learning researcher to earn that kind of money,” she points out. “But if you can solve real problems with AI, companies will pay for that. You can come from ops, product, design—doesn’t matter.”
When you think in systems, not just outputs, you stop trading time for money. You start getting paid for what you automate, not just what you do.
“You build workflows that save real time and money,” she adds. “That kind of agency gets recognized—and paid—for what it brings to the bottom line.”
Common challenges and how to overcome them
If you’ve been circling the idea of becoming an AI engineer but still haven’t started, odds are you’re hitting one of these blockers. Here’s what they are, and how to get past them:
1. Thinking you need to be a coder (or a math genius)
One of the biggest myths people should through out the window: that AI engineering is only for machine learning PhDs.
In reality, most working AI engineers aren’t building models from scratch. They’re working around them… using ChatGPT, Make, APIs, and no-code tools to design systems that solve real problems.
As for what it really looks like behind the scenes? “You’re not training GPT-4. You’re building around it,” says Mehreen. “AI engineering is really about your ability to map workflows and build smart systems, not your ability to code them.”
2. Getting stuck in prompt-chasing mode
Everyone starts with prompts, and that’s normal. But at some point, you’ve got to move past the copy-and-paste stage.
Downloading 100 prompt libraries won’t teach you how to build a system that runs without you. What matters, in the end, is building something that actually gets used.
Can this process run without your constant input? Can it deliver value to someone else?
Your ability to answer these questions is how you shift from prompting to engineering. The main thing to note? A prompt should always trigger a flow, a function, or a decision that saves time or makes money, whether inside your company, across your teams, or for your clients.
If prompts are the input, then pipelines are the product.
3. Waiting to feel “ready” before building something
This one’s sneaky. You bookmark tutorials, sign up for courses, maybe even block off “AI learning” time on your calendar. But you never actually ship anything.
Well, here’s the truth: the only way to get good at AI engineering is to build in real time. You learn faster when the stakes are real. Yep, that is, when the system you’re building has to work for your team, your business, or your workflow.
As Mehreen says, “AI engineering isn’t about learning everything at once. It’s about learning just enough to build something.”
The fix for your builder’s block? Pick one problem, one workflow, and build a scrappy version of your solution that works. Then, improve the whole thing next week.
4. Trying to learn everything at once
The AI space moves fast. Every week, there’s a new model, tool, or trend. If you’re not careful, all that noise can slow you down.
Because the truth is, you don’t need to chase every headline. You need to get clear on your use cases. To get started properly, ask yourself:
- “What do I need to automate?”
- “Where can I save time?”
- “What system breaks down the most in my day-to-day?”
Start small to start somewhere. And it’s only a matter of time before your consistency stacks up.
If you improve your work just by 3% weekly, you’ll be 4.5x more productive by year’s end.
— Vykintas Glodenis, chief AI transformation officer at Mindvalley
5. Not treating your builds like assets
It’s easy to see AI experiments as temporary, just a side hobby, or something “fun” you try, then forget completely later. But real AI engineers treat their builds like assets that grow more valuable with time.
That Notion automation you made? It just saved you 6 hours this week. That custom GPT for onboarding? It’s scaling your customer experience without scaling headcount.
In other words, any of these AI engineering projects is proof of what can be synthesized into scalable working functions.
So, follow Mehreen’s advice on this: don’t keep all ideas in your head. Instead, just do the work, bit by bit, every day. “Build your workflows. Document your systems. The value is in the repeatability.” And that’s how you create a portfolio of leverage.
Your must-have AI engineering toolkit and resources
If you’re serious about becoming an AI engineer, your inputs matter as much as your outputs. The right ideas, tools, and voices in your feed can sharpen how you think, build, and make decisions.
Start with a few trusted sources, like the ones below, to keep well-informed as you cultivate the builder in you:
Courses
From workflows to agents, these Mindvalley programs will have you training your brain the same way you’d train a model—by doing:
1. Amplify with AI
Available on Mindvalley, this 21-day program teaches you how to solve real-world problems using GPTs, Make, Midjourney, and no-code tools.
With guidance from Vishen, Vykintas, and Manon Dave (Mindvalley’s chief product and innovation officer), you’ll build automations, train AI assistants, and start stacking your unique systems. No technical background required.
2. 3 Days to Mastering ChatGPT
Guided by Andri Peetso, the co-founder of Conturata-AI and Whomesome, this Mindvalley course dives deep into the myriad ways that AI helps with productivity. It’s ideal if you’re ready to get real results from ChatGPT in your day-to-day work beyond just prompting.
3. AI Mastery
Led by Vishen and other world-renowned AI experts, you’ll build agentic systems, automate entire workflows, and scale your strategy across content, ops, and decision-making.
Think of this program as a premium path for creators, solopreneurs, and operators ready to go beyond the basics. No technical software background needed. All you’ve got to do? Maintain your desire to master vibe coding all the way through.
Podcasts
These shows pull back the curtain on how AI engineering works in the real world… from agents and infrastructure to the people building the future:
- Latent Space. Hosted by Shawn “Swyx” Wang (a former engineer at Amazon and AirBnB) and Alessio Fanelli (an investor at Decibel), this show dives deep into foundation models, AI agents, GPU infrastructure, and more. Expect interviews with the world’s best builders shaping the future of AI.
- The AI Breakdown. Run daily by AI strategist and tech/crypto enthusiast Nathaniel Whittemore, this podcast gives sharp, digestible rundowns of AI news, policy shifts, and industry takeaways. Good if you like staying informed without doom-scrolling.
- Practical AI. Here, Chris Benson, a chief AI strategist at Lockheed Martin, along with data scientist and academician Daniel Whitenack, turns complex AI into practical workflows. Think: tools, ethics, and real-world use cases across industries.
- Machine Learning Podcast. Hosted by Tobias Macey, the Data and Infrastructure Platform Engineering team lead at MIT Open Learning, it’s a spin-off focused on the production pipeline of machine learning. It’s the right show for anyone interested in building AI systems in unique real-world scenarios.
- Hard Fork. Kevin Roose, tech columnist at The New York Times, teams up with Platformer founder Casey Newton to unpack how AI is reshaping business, creativity, and culture. Always on: sharp commentary and research that hits.
- No Priors. This podcast is a power meeting between ex-Greylock and Conviction founder Sarah Guo and former Airbnb and Stripe investor Elad Gil. Together, they talk with the sharpest AI founders and researchers about how generative AI is redefining product, infrastructure, and the speed of innovation.
Books
Think sharper. Build smarter. These reads will wire your brain the right way:
- Architects of Intelligence by Martin Ford. A future-forward look at where AI is going, featuring insights from leaders at DeepMind, OpenAI, and Google Brain. Reads like a map of what’s coming.
- The Alignment Problem by Brian Christian. It’s a must-read for understanding the tension between building smart systems and making them safe, ethical, and aligned with human values.
- Reprogramming the American Dream by Kevin Scott. Written by Microsoft’s chief technology officer, this book unpacks how AI can empower the everyday person. Not just Big Tech. Strong case studies, zero fluff.
- AI 2041 by Kai-Fu Lee and Chen Qiufan. Part fiction, part analysis. Explore ten vivid short stories imagining AI in everyday life that are followed by breakdowns from one of the sharpest AI minds in the world.
- You Look Like a Thing and I Love You by Janelle Shane. It’s perfect for when you need a brain break without losing the thread. This book uses humor to demystify neural networks, showing what they can and can’t do.
How long does it take to become an AI engineer?
The short answer? It depends on where you’re starting and how deep you want to go.
But here’s the reality check:
If you’re learning part-time, from a non-technical background, and focusing on no-code tools and applied workflows, you can expect to start shipping real AI projects in 6 to 12 weeks.
But if you’re aiming for technical AI engineering roles (think: building models, Python-heavy jobs, ML pipelines), expect six months to over a year of consistent, structured learning.
Still, the fastest route to mastering AI in education?
- Solve one real problem a week using AI tools like ChatGPT, Make, or Notion.
- Stack those builds.
- Then learn from each iteration you do.
That’s right—experience is the best way through.
As Mehreen would remind you, time and again: You don’t need to learn everything. You need to build something. That’s how your skills catch up.”
Unleash your limitless
Now you’ve got the steps. What comes next is simple: start building systems that solve the grind, while you stack skills that shift your career up a level. And once you ship that first working loop? You don’t go back.
That’s where Mindvalley’s Amplify with AI program can help you make that first step toward that reality. Led by Vishen and his team of AI experts, it’s a 21-day sprint that gets you building with globally recognized AI tools like ChatGPT, Make, and Midjourney… so that you can build and deliver real-world workflows from scratch.
No coding background or skills necessary.
Inside the program, you’ll discover how to:
- Train AI assistants to think and act like your team,
- Turn tasks into automations that scale what you already do,
- Build the systems that free your time and multiply your output, and
- So much more.
Still unsure if it’s the right course for you? Well, the first lesson’s free, so no rush, no risk. What’s essential is you test-drive the “build before you’re ready” mindset for yourself.
And once you do, you’ll get why Justin Ong, a Hangzhou-based entrepreneur, would thank AI for changing his life. He was running his business on willpower alone, fighting fires, chasing deadlines, and getting stuck in the weeds along the way. But then, as if by sheer luck, he stumbled upon the wonders of automation.
“I leaned into AI to take myself out of the bottleneck. That’s when everything shifted.”
— Justin Ong, Mindvalley member
You can say that life has never been the same since for Justin. And just like him, you, too, will realize you’re not here to stay stuck in the loop. No—you’re here to build the system that frees you from it. One step at a time, with Mindvalley at your side.
Welcome in.
