16 March 2026 | 3 min read

What Recruiters Should Know About Hiring AI Engineers

hiring AI engineering recruiting career advice tech hiring

The Hiring Problem

AI engineering is a new discipline, and the hiring process has not caught up. I regularly see job postings that conflate AI engineering with data science, machine learning research, or prompt engineering. These are related but distinct fields, and the confusion leads to bad hires and frustrated candidates on both sides.

As someone who both hires AI engineers and is one, here is what I wish recruiters understood about the role.

AI Engineering Is Software Engineering

The most important thing to understand is that AI engineering is a specialization of software engineering, not a branch of data science. An AI engineer builds production software systems that incorporate AI capabilities. The emphasis is on engineering: building reliable, scalable, maintainable systems.

This means the core skills you should look for are:

Strong software engineering fundamentals (architecture, testing, deployment)
API design and integration experience
Database design and management
Production operations and monitoring
Plus AI-specific skills: prompt engineering, model selection, evaluation, and cost optimization

What to Look for in Portfolios

The best signal for AI engineering ability is a portfolio of production systems. Not research papers, not Jupyter notebooks, not ChatGPT prompt collections. Look for:

Deployed applications: Systems that are running in production and serving real users
End-to-end ownership: Did they build the whole pipeline, from data ingestion to deployment?
Error handling and monitoring: How do they deal with failure? This separates demo builders from production engineers
Cost awareness: Do they think about API costs, token optimization, and model selection based on the task?

Red Flags in AI Engineering Candidates

Only experience with AI is using ChatGPT or similar interfaces
Cannot explain how they would handle model failures or rate limits
No understanding of the cost implications of different model choices
Heavy use of frameworks like LangChain without understanding what happens underneath
No production deployment experience

Stop Requiring PhD Credentials

Many AI engineering job postings require a PhD in machine learning or a related field. This requirement eliminates excellent candidates and is not actually correlated with AI engineering ability. AI engineering is about building systems that use models, not about creating new models from scratch.

Some of the best AI engineers I know come from:

Backend software engineering
DevOps and infrastructure
Full-stack web development
Enterprise platform development (like my CRM background)

What matters is the ability to build reliable software and the willingness to learn AI-specific concepts. A strong software engineer can learn prompt engineering in weeks. A prompt engineer without software skills will struggle to build production systems for years.

Technical Interview Tips

If you are designing an interview process for AI engineers, here is what actually tests the skills that matter:

System design: Give them an AI application scenario and ask how they would architect it. Look for consideration of error handling, cost, scalability, and monitoring
Code review: Show them an AI pipeline with bugs and ask them to identify issues. This tests real-world debugging ability
Production scenarios: "Your LLM API returns a 429 error during peak traffic. What do you do?" This tests operational thinking
Trade-off discussion: "Would you use Claude or GPT-4o for this task? Why?" Look for nuanced reasoning, not brand loyalty

Compensation Realities

AI engineers with production experience are in high demand. If your compensation package is benchmarked against general software engineering roles, you will lose candidates. The market has moved, and AI engineering commands a premium because the combination of software engineering skills and AI expertise is genuinely rare.

Remote work options also matter disproportionately to AI engineers. The work is highly asynchronous and does not benefit much from in-person presence. Requiring London office attendance five days a week limits your talent pool unnecessarily.

The best AI engineer for your team might not have "AI" anywhere on their current CV. They might be a senior backend engineer who has been building AI systems in their spare time. Look for builders, not titles.

The Bottom Line

Hire for software engineering skills first and AI-specific skills second. Look for portfolios of production work, not academic credentials. Offer competitive compensation and remote flexibility. And please, stop putting "5 years of experience with GPT-4" in job requirements for a model that has existed for two years. The field is moving too fast for traditional hiring patterns to work.