Cloud Engineer vs AI Engineer: Why I’m Learning Both (2026)

Look, I need to be straight with you about something that’s been bugging me lately. I’m Likhon Hussain, and I’ve been working as a cloud engineer for a while now. Almost every week, someone messages me on LinkedIn asking the same panicked question: “Should I even bother learning cloud engineering, or is AI just going to replace everything?”

I totally get where this anxiety comes from. You’re scrolling through your feed, and every other post is screaming about how AI is taking over. It’s exhausting. But here’s the thing after spending years in the trenches building cloud infrastructure and watching the AI boom happen in real-time, I’ve realized most people are freaking out about the wrong thing.

The Question Everyone’s Getting Wrong

Here’s what actually keeps me up at night (besides those 3 AM production alerts): it’s not about AI versus cloud anymore. That ship has sailed.

When I’m deploying infrastructure at work, guess what I’m increasingly deploying? AI workloads. Machine learning pipelines. Model training environments. Every single one of those fancy AI systems everyone’s worried about? They’re running on the cloud infrastructure that people like me build.

Last month, I helped set up a Kubernetes cluster specifically for running LLM inference. The AI team needed someone who understood how to scale infrastructure, manage costs, and keep things from crashing when traffic spiked. That’s cloud engineering work. For an AI system.

See what I mean? These worlds are crashing into each other.

What You Actually Need to Learn (From Someone Who’s Been There)

Starting Out: The Basics That Actually Matter

When I started in cloud engineering, I made a huge mistake. I spent three months reading networking books before I ever touched AWS. Don’t do that. Seriously.

Here’s what I wish someone had told me: learn by building. When you’re studying networking concepts, spin up a VPC at the same time. Create subnets. Break things. Fix them. That’s how it actually sticks.

For folks going the AI route, yeah, you need Python. But more importantly, you need to understand why things work, not just that they work. I’ve interviewed AI engineers who could explain gradient descent but couldn’t tell me why their model was giving garbage predictions on real-world data. Theory’s great, but it means nothing if you can’t apply it.

Getting Good: Where Things Get Interesting

Once you’ve got the fundamentals down, here’s where paths used to diverge. Used to.

As a cloud engineer, I had to learn Infrastructure as Code. Terraform became my daily driver. Docker and Kubernetes went from scary buzzwords to tools I couldn’t live without. And honestly? Learning to write code that manages infrastructure instead of clicking through console dashboards changed everything for me.

I remember this one project where I automated our entire deployment pipeline with Terraform. What used to take two days of manual work now happens in 20 minutes. That’s the kind of skill that makes you valuable.

For AI engineers, you’re picking up frameworks like TensorFlow or PyTorch. But here’s something I’ve noticed working alongside AI folks: the ones who understand cloud deployment get promoted faster. Why? Because building a model that only works on your laptop doesn’t help the company make money.

Going Deep: The Specialization Nobody Talks About

Here’s where it gets real interesting for both paths.

I’ve started diving into AI deployment on cloud platforms. Not because I want to become an AI engineer, but because that’s literally what my job requires now. Setting up SageMaker environments, optimizing inference costs, managing vector databases this stuff is increasingly part of cloud engineering work.

On the flip side, AI engineers are learning cloud architecture because they need to deploy their models at scale. We’re meeting in the middle.

And can I be honest about something? If you’re a cloud engineer who refuses to learn anything about AI deployment, you’re going to struggle. I’ve seen it happen to colleagues who thought they could just keep doing the same old VM management forever. The industry moved on without them.

The Money Talk (Because We’re All Thinking It)

Okay, let’s talk salaries. As a cloud engineer in the US, you’re looking at roughly $130K to $145K for mid-level roles. I’m doing better than that now, but it took time and constantly learning new skills.

AI engineers are pulling in slightly more $135K to $175K on average. Senior folks in both fields are hitting $200K+. And yeah, you’ve seen those wild stories about people making millions, but let’s be real: that’s not most of us.

Here’s what I find fascinating though: location makes a massive difference. I know people with similar skills making $150K in Austin and $220K in San Francisco. Same work, different zip code.

But the real sweet spot? Engineers who know both cloud and AI are writing their own tickets right now. Companies are desperate for people who can architect AI systems on cloud infrastructure. I’ve had recruiters reach out offering 20-30% more than my current salary specifically because I’ve been building up my AI deployment skills.

The Job Market Reality (From Someone Living It)

I’m not going to sugarcoat this it’s tough out there right now. Way tougher than when I started.

The bar’s higher. You can’t just know AWS basics anymore. You need to understand security, cost optimization, automation, and increasingly, how to support AI workloads. That’s just for entry-level positions.

But here’s the flip side: the demand is absolutely real. My company can’t hire cloud engineers fast enough, especially ones who understand anything about AI infrastructure. We’ve had positions open for months.

And it’s not just tech companies. I’ve got friends working in healthcare, finance, even retail, all building cloud infrastructure for AI systems. The opportunities are spreading across industries.

How I Actually Learned This Stuff (No BS)

Tutorial hell is real, and I lived in it for way too long when I was starting out.

You know what actually worked? Building projects that broke. Staying up until 2 AM trying to figure out why my Lambda function wouldn’t trigger. Accidentally racking up a $300 AWS bill because I forgot to shut down some instances (yeah, that happened).

Those mistakes taught me more than any video course ever did.

Every project I built, I documented. Not because I’m super organized (I’m not), but because I knew I’d forget how I solved problems. That documentation became my portfolio. When I was job hunting, I could show actual work, not just certifications.

And when things broke which they always did I learned to embrace it. Good engineers aren’t people who never mess up. We’re people who get good at debugging fast and staying calm when production’s on fire.

The Human Side Nobody Mentions

Here’s something they don’t teach you in technical courses: you’re going to spend a lot of time talking to people.

I’m in meetings constantly. Daily standups. Planning sessions. Explaining to non-technical stakeholders why we need to spend $50K on infrastructure upgrades. This isn’t what I expected when I got into engineering, but it’s huge part of the job.

The engineers I respect most? They can explain complex systems in plain English. My CEO doesn’t care about Kubernetes pods or VPC peering. She wants to know if the system’s reliable and what it costs. Being able to translate technical details into business language is incredibly valuable.

What I’m Seeing Happen Right Now

I’m literally watching these roles merge in real-time.

Last week, I saw a job posting for “Cloud AI Engineer.” Not cloud engineer or AI engineer both. My company’s talking about restructuring teams to have “full-stack infrastructure engineers” who handle everything from cloud architecture to AI model deployment.

This isn’t some future trend. It’s happening right now.

The teams I work with are becoming more cross-functional. AI engineers are learning cloud architecture. Cloud engineers (like me) are learning about model deployment and vector databases. The lines are blurring fast.

So What Should You Actually Do?

Here’s my honest advice: pick the one that excites you more right now, but don’t ignore the other one. If you love working with data and algorithms, start with AI engineering. But please, learn how to deploy your models properly. Work on AWS or Google Cloud. Understand how infrastructure scales.

If you’re more interested in building reliable systems (that’s me), go the cloud route. But start learning about AI workloads. Understand what data scientists need from infrastructure. Learn how model training differs from regular application workloads.

I started as a pure cloud engineer. Now? Half my work involves AI infrastructure. I didn’t plan it that way the industry just moved there, and I moved with it.

My Last Line

The job market’s challenging. Competition’s fierce. You’re going to face rejections. I certainly did. But companies are also spending billions on cloud infrastructure for AI systems. They need people who get both sides. That’s not going away.

Whether you start with cloud or AI, the goal’s the same: position yourself at the intersection. That’s where the interesting problems are. That’s where the money is. And honestly, that’s where the work stays engaging. I’ve been doing this long enough to know that the engineers who thrive are the ones who refuse to stay in their lane.

They learn adjacent skills. They stay curious. They adapt. The convergence of cloud and AI isn’t some distant future. I’m living it every day. The only real question is: are you going to be ready when your company needs someone who understands both? Because trust me, they will.