AI engineer: the complete hiring guide

Every AI engineer job description I see lists the same 47 skills.

Python, PyTorch, TensorFlow, transformers, RAG, vector databases, fine-tuning, prompt engineering, LangChain, cloud platforms, MLOps, Docker, Kubernetes. The list goes on. Each one supposedly required.

Here’s what nobody says: half those tools will be different in six months.

Demand for AI professionals grew 74% annually according to LinkedIn, making it one of the fastest-growing job categories. But the skills changing underneath that growth? Completely different story.

Why most AI engineer job descriptions miss the point

The typical AI engineer job description treats the role like traditional software engineering. List every technology, demand years of experience with each, require a computer science degree, add a few buzzwords about innovation.

Wrong approach entirely.

AI engineering is fundamentally different. The tools change monthly. Jobs requiring specialist AI skills grow 3.5 times faster than all other jobs, but the specific skills within those jobs? Completely unstable.

I’ve watched companies spend three months finding the perfect candidate who knows their exact stack. By the time they start, the company has moved to different tools. That detailed requirements list you spent weeks perfecting? Already outdated.

The real problem: you’re hiring for knowledge that expires. What you need is someone who learns faster than your technology changes.

What matters: learning over knowing

Gartner predicts that by 2027, 75% of hiring processes will include tests for workplace AI proficiency. But proficiency in what? The frameworks today or the frameworks tomorrow?

Better question: can this person figure out whatever comes next?

Here’s what I look for when building AI teams. First, can they explain complex technical concepts in plain English? If someone cannot make me understand how a transformer works without using jargon, they don’t understand it themselves.

Second, do they show genuine curiosity? When you mention a tool they haven’t used, do they ask questions or pretend they know it? The pretenders are easy to spot. They nod along. The learners start probing.

Third, have they built anything end-to-end? Not just training models. Building complete systems. Most AI engineers can train a model. Far fewer can deploy it, monitor it, debug it in production, and maintain it over time.

Research shows 87% of companies already have a skills gap according to McKinsey. That gap keeps growing because companies keep hiring for specific tools instead of learning capacity.

The field moves too fast. You need people who can learn new frameworks in days, not months.

The interview framework that works

Forget the standard algorithm questions. Those test computer science fundamentals, which matter, but tell you nothing about AI engineering ability.

Here’s a better approach. Give them a real problem they haven’t seen before. Something like: build a system that answers questions from our documentation. Use whatever tools you want. Explain your choices.

Watch what happens. Do they immediately reach for the most complex solution? Red flag. The best engineers start simple.

Do they ask about the actual use case before picking tools? Good sign. Shows they’re thinking about the problem, not just the technology.

Do they explain tradeoffs? Excellent. Anyone can propose a solution. Understanding what you’re giving up requires real depth.

Common mistakes when hiring AI engineers include not testing applied skills. According to Built In, while most candidates should have theoretical knowledge, not all of them can code well. Test both.

Your coding challenge should feel like actual work. Not LeetCode problems. Give them a small dataset, a vague requirement, and see how they handle ambiguity. AI engineering is mostly dealing with unclear requirements and messy data.

For system design questions, focus on AI-specific challenges. How would you handle model versioning? What happens when your model starts degrading in production? How do you monitor for data drift?

These questions reveal whether someone has built production AI systems or just followed tutorials.

Your first 90 days checklist

Most companies throw new AI engineers into projects immediately. Predictable result: they spend months spinning up on context they should have gotten in weeks.

Better approach: structured onboarding with clear milestones.

First 30 days: Learning. New hires should focus entirely on understanding your domain, data, and existing systems. Assign them a buddy from the team. Have them document what they learn. Documentation forces clarity and creates useful artifacts for the next hire.

Onboarding best practices from ML engineering teams suggest pairing new hires with experienced employees and providing regular check-ins during this period.

Days 30-60: Building. Give them a small, contained project with clear success criteria. Something that ships. Not critical path, but real. They should be contributing code that goes to production, even if it’s minor.

Days 60-90: Owning. By now they should take ownership of a component or system. Not huge scope, but genuine responsibility. This is where you see whether they can operate independently.

The key: clear expectations at each stage. New hires shouldn’t guess what success looks like.

Also critical: regular check-ins. Not micromanaging. Quick syncs to answer questions, remove blockers, course-correct early. Most problems in the first 90 days come from lack of communication, not lack of ability.

The red flags everyone ignores

Here’s what predicts failure. First: candidates who cannot explain their past projects clearly. If they built something significant, they should be able to walk you through the problem, their approach, the results, and what they learned.

Vague answers suggest they were peripherally involved or don’t understand what they built.

Second: overconfidence about AI capabilities. Anyone claiming their model is 99% accurate or that they can solve your problem easily hasn’t done enough real AI work. The best AI engineers are deeply aware of limitations.

Third: lack of practical deployment experience. According to research, candidates frequently overstate their involvement in past projects. During interviews, details become vague when pressed about actual implementation.

If someone has only worked with clean datasets in notebooks, they’ll struggle with real-world messiness. Ask about the hardest production issue they debugged. Their answer tells you everything.

Fourth: no curiosity about your specific problem. Candidates who immediately start proposing solutions before understanding your constraints, data, or requirements? They’re selling, not solving.

The best candidates ask more questions than they answer in early conversations.

What your AI engineer job description should say

Forget the 47-skill list. Try this instead.

We need someone who can learn any AI framework in a week, explain technical decisions to non-technical stakeholders, and build systems that work in production.

You should have built at least one complete AI system end-to-end. Doesn’t matter which tools you used. We care how you think, not what you memorized.

You should be comfortable with ambiguity. Most of our problems don’t have obvious answers. You will need to figure out what to build before you can build it.

You should write code that other people can maintain. We don’t do clever. We do clear.

This kind of AI engineer job description scares away people who just want to add buzzwords to their resume. Good. You don’t want those people anyway.

It attracts people who build things and can learn whatever they need to learn. Those are the people who will still be valuable when your entire tech stack changes next year.

Because it will change. The only question is whether your team can change with it.