How I'd Become an AI Engineer in 2027, Starting From Zero

Most people are preparing for the wrong job

When people hear the phrase "AI engineer", they picture someone training enormous models from scratch, reading research papers and stacking up certificates. That job exists, and it pays well. But it is not the opportunity I would chase today, because it is a tiny number of seats held by people with PhDs and years of research behind them.

The much bigger opportunity is this: the intelligence already exists. ChatGPT, Claude, Gemini and Grok are all extremely capable, and the labs behind them are fighting a benchmark war you will never take part in. What companies are short of is people who can take those models and build useful, working software with them.

The data backs this up. LinkedIn found that AI job postings in the US have roughly doubled since 2023, and "AI engineer" has overtaken "machine learning engineer" as the most common AI role on the platform. That is a big shift. The AI engineer of 2027 is mostly a really good software engineer who has learned the AI ecosystem.

I say this as someone who did not study computer science. I graduated in accounting and finance, taught myself to code in 2019, went on to build companies, and now run Sigmaschool, where we train people to become software engineers who build with AI. If I had to compress everything I have learned into the fastest reasonable path, this is it.

Step 1: Stop "learning AI" (at least at the start)

"Learn AI" is too vague to act on. You could study neural networks, transformers, computer vision, fine-tuning, PyTorch, reinforcement learning, agents, RAG or evals. Six months later you are still studying and you have not built anything anyone wants.

A far more useful question is: what does an AI engineer actually get paid to do? Imagine a company wants a system that reads customer emails, checks each customer's order history, decides what needs to happen and drafts a reply. Can you build that? That is the job. Nobody hiring you cares how many AI courses you watched. They care whether you can take a messy, real problem that is costing them money and turn it into working software.

Once you see it that way, learning gets much easier. The list of shiny things to study is effectively infinite, but your goal is not infinite. Your goal is to build a skill that adds real value and gets you paid. So you learn whatever helps you solve the problem in front of you, and you ignore the rest for now.

Step 2: Learn the boring skill that makes everything else easier

Before agents, RAG or anything fashionable, I would learn normal software engineering and get properly good at it. A lot of people want to skip this because AI can write so much code now. I think that is a big mistake.

You do not need to become someone who writes everything from memory. You do need to understand how software works. Pick Python or JavaScript and go. Learn variables, functions, loops, basic data structures, APIs, Git, databases and how a backend works. Then build small things: call an API, save something to a database, create a basic backend, deploy something to the internet. The goal is to reach the point where software stops feeling like magic. If you are not sure where to start, I wrote a separate guide on choosing your first programming language.

And yes, use AI while you learn. Telling a beginner not to use Claude or Codex is like telling an accounting student not to use Excel. The rule is simple: use it, but do not become useless without it. There is a world of difference between "build this whole thing for me" and "here is what I am trying to build, help me write this function, then explain what you changed and why." Sooner or later something will break, and the person who has to understand what went wrong needs to be you. We go deeper on this in why you should not become dependent on AI.

Step 3: Add the AI layer in this order

Once you can build basic software, start adding AI, and keep it practical. This is the order I would learn things in, because each step builds on the one before.

Step 4: Build something you would actually use

This is where I would stop watching courses. At some point another tutorial adds almost nothing compared with building something and getting stuck. And please, do not build another basic ChatGPT clone. Build something where the AI gets a real job done.

Here is an example from our own company. We are building an AI recruitment assistant. A recruiter uploads 100 CVs. The system reads them, pulls out each candidate's experience, checks them against the job requirements, perhaps researches candidates with tools, and produces a shortlist. Before anything gets sent, a human recruiter approves it.

Build something like that and every concept suddenly makes sense. APIs make sense because you need them. Databases make sense because you need to store candidates somewhere. RAG makes sense because the AI needs the right information to make decisions. Tool calling makes sense because the agent has to connect different systems. You are learning in context, the way you would at work, instead of trying to hold abstract theory in your head. If you want ideas, here are some projects that actually get interviews.

Step 5: Expect your demo to fall apart, and learn evals

Your first demo will work beautifully. You show a friend, they are impressed. Then a real user types something slightly odd and the whole thing collapses. Welcome to AI engineering. These systems are probabilistic: give the same system a slightly different situation and you can get completely different behaviour.

The fix is evals, which are tests for your AI system. You write lots of examples of what good looks like and check whether the system does it. Did it choose the right tool? Did it retrieve the right information? Did it make something up? Did it follow the rules? Then, when you change the prompt or swap the model, you can measure whether things got better or worse instead of guessing.

Around the model sits a wider system: instructions, tools, memory, permissions, retries, logging, evals, rules and human approvals. People increasingly call this the agent harness, and I think understanding it is one of the most important skills heading into 2027. You do not control how smart the models get. OpenAI, Anthropic and Google are fighting that battle. Your job is to build a system around a capable model that works reliably in production. That is where a lot of engineering is moving.

Step 6: Do not compete with AI at writing code

I would not build a career on being the fastest typist in the room. AI is getting ridiculously fast at writing code, and GitHub is already rebuilding parts of its own infrastructure for what it calls agent-scale development, where humans and coding agents work on the same repositories at the same time.

So the valuable skill moves up a level. Can you understand the problem? Can you break it into pieces? Can you give the AI the right context? Can you decide what should be built, review what the AI produced, and tell when it is wrong? That is what I would practise. Think of yourself less as the person laying every brick and more as the person who understands how the building should be built. You still need to understand bricks. Laying them quickly is just no longer your biggest advantage. This is the shift we describe in what AI-native developers actually do.

Step 7: Your portfolio has one job

Once I had the skills, I would build two or three really good projects. Not fifteen. A portfolio has one job: make someone believe you can already do the job.

So I would not just paste GitHub links into my CV. I would deploy each project, record a short demo, show real people using it, explain the problem, explain how I designed it, and most importantly explain what went wrong and how I fixed it.

When someone tells me in an interview that they built an AI agent, I say, "Cool." Then I ask: what happens if it picks the wrong tool? How are you evaluating it? Show me your evals. What happens when the model returns invalid data? Why did you use RAG here? How do you stop it doing something it should not? That is when I find out whether you made the decisions yourself or followed a tutorial. Your project is the proof, so treat it that way.

Step 8: Get hired by showing, not asking

Being good is not enough any more. People need to know you are good. With zero experience, I would not send 500 identical applications and hope. I would pick companies I genuinely want to work for, find out what they are building, and build something related to their problem.

Then I would share it publicly, write about what I learned, and reach out to the engineers and managers, or the founder if it is a smaller company. I would go to meetups and hackathons. There is a huge difference between "I want to break into tech, please give me a chance" and "I saw you are building X, so I built a smaller version. Here is what I made and what I learned." The first asks someone to take a gamble on you. The second gives them evidence that you can add value. More on this in our guide to getting a tech job without experience.

Learn a little, build, get stuck, repeat

So if I started tomorrow, the path would look like this: enough Python or JavaScript and software engineering to build things, then LLM APIs, structured outputs, tool calling, RAG, state, agents, evals and the harness around them.

But I would not learn all of that first. I would learn a little, build, get stuck, learn the next thing and build again. The biggest mistake I see is people spending six months preparing to start. Start embarrassingly early.

The World Economic Forum estimates that 39% of workers' core skills will change by 2030. The specific framework you memorise today matters less than your ability to learn quickly, build with AI, and turn a vague problem into something useful. That is the person I would try to become.

An honest caveat: this path is not a shortcut. Software fundamentals take real, structured hours, and doing it alone is where most people stall, because nobody tells you when your understanding has a hole in it. That is exactly why our AI-Native Software Development Programme puts fundamentals first and has mentors review everything you ship. If you want to test whether this kind of work suits you before committing, start with the free trial.