You've seen the job title everywhere: AI Engineer. Maybe a recruiter has already reached out, or maybe you're eyeing the role and wondering if you're "AI enough" to apply. This series is your interview prep — practical, honest, and built for busy developers. Let's start with what the role actually is and what the interview actually looks like.
What an AI Engineer actually does in 2026
Here's the most important thing to understand before any interview: an AI Engineer is not a researcher. Nobody expects you to invent new model architectures or publish papers. An AI Engineer builds production software that uses AI models — usually large language models — to solve real business problems.
In practice, the day-to-day work looks like this:
- LLM integration — calling models like Claude or GPT from application code, handling streaming, errors, retries, and cost.
- RAG pipelines — connecting models to company data so they answer from facts instead of guesses. (New to RAG? Here's our plain-English explainer.)
- Agents and tool calling — letting a model take actions: search, run code, call APIs, often via protocols like MCP.
- Evals — measuring whether the system actually works, because "it looked good when I tried it" doesn't survive production.
If you've been a backend, full-stack, or .NET developer, notice something: three of those four bullets are software engineering. APIs, pipelines, data, testing. The AI-specific knowledge is a layer on top of skills you already have — and that layer is exactly what this series teaches you to talk about confidently.
Why this role, why now
The market data is unusually clear. AI Engineer is LinkedIn's #1 fastest-growing job in the US for 2026, with postings up 143% year over year. Roughly 41% of active US tech postings now require AI skills or are AI-specific roles, and AI/ML/data roles as a group grew about 163% year over year.
And here's the number that should genuinely encourage you if you're early in your career: the median prior experience of people hired into these roles is about 3.7 years. This is not a "10 years of ML required" field. It's so new that nobody has ten years of LLM production experience. The door is open, and it's open widest right now.
Tip: When a job posting says "AI Engineer," read the responsibilities, not the title. If it lists model training from scratch and CUDA kernels, that's a research/ML-platform role. If it lists RAG, prompting, agents, and evals — that's the role this series prepares you for, and it's the vast majority of postings.
The typical interview loop
Loops vary by company, but a clear pattern has settled in. Most AI Engineer interviews look something like this:
| Stage | What they're checking | Covered in |
|---|---|---|
| Recruiter screen | Can you describe AI work you've done in plain language? | This post + Part 7 |
| Technical: LLM fundamentals | Do you understand how models work — tokens, context, hallucination? | Part 2 |
| Technical: prompting & context | Can you get reliable behavior out of a model, and measure it? | Part 3 |
| System design: RAG | Can you architect an AI system, not just call an API? | Part 4 |
| Agents & tool calling | Do you understand agents, tools, and when not to use them? | Part 5 |
| ML basics check | Do you know just enough classic ML vocabulary to not be lost? | Part 6 |
| Behavioral | How do you work, learn, and handle ambiguity? | Part 7 |
The RAG system-design round deserves special mention: it has become the signature round of AI Engineer interviews — the new FizzBuzz, except it actually predicts job performance. That's why Part 4 is the longest post in this series.
How to prepare when you're busy
You don't have a month of free evenings, and this series doesn't assume you do. Each part is built around a handful of real interview questions with answers explained deeply enough that you understand them — because interviewers always follow up, and memorized answers collapse under the first "why?"
Every question comes with a small 🧪 Try it exercise. Do them. They take minutes, not weekends, and they are the actual prep: by the end of the series you'll have poked at tokenizers, broken and fixed JSON output, built a tiny eval set, and assembled a miniature RAG pipeline. When an interviewer asks "have you built anything with LLMs?", those exercises are your honest yes.
Red flag: The fastest way to fail this loop is to only read about AI. Interviewers can tell within two follow-up questions whether you've actually called a model API and hit its sharp edges. Thirty minutes of hands-on beats three hours of YouTube.
Honest expectations
Let's set the bar accurately, because both overconfidence and panic will hurt you.
You do not need: a PhD, a math-heavy ML background, or research experience. You don't need to explain transformer internals beyond a conceptual level, and you'll learn that level in Part 2.
You do need: solid general engineering skills, a working understanding of how LLMs behave (and misbehave), and — this is the one people skip — evidence that you've built something, even something small. A weekend RAG over your own notes counts. A little tool-calling experiment counts. The try-its in this series produce exactly this evidence, on purpose.
It's normal to feel like an impostor here. Everyone in this field is newer to it than they look. The candidates who get offers aren't the ones who know everything — they're the ones who understand the fundamentals clearly and can reason out loud about trade-offs. That's a learnable skill, and it's what the rest of this series drills.
Ready? We'll start where every technical round starts: how these models actually work. On to Part 2: LLM Fundamentals Questions (and Answers That Impress).