Interviewing AI Engineers: What Non-Technical Hiring Managers Should Know

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Deliveroo

Jul 4, 2025

Ever been in a job interview with a software engineer and thought, “I have no idea what they just said”? You’re not alone.

And for non-technical hiring managers, interviewing AI talent can often seem like interpreting a foreign language. You’re the one charged with finding the perfect candidate for a deeply technical position — even if you may not speak the technical lingo. It’s a high stakes game, particularly when a single bad hire can slow down an entire project.

The ability to understand what really matters in an AI engineer — not just the buzzwords — is essential. From technical expertise to problem-solving skills and team fit, there’s more to consider than just code.

This guide is here to help. Whether you are building an AI team from the ground up or expanding an existing team, we’ve got you covered with what you need to know to interview AI engineers with confidence and effectiveness (even if you aren’t a techie yourself).

Why Non-Technical Hiring Managers Need to Understand AI Engineer Roles

AI engineers have transitioned beyond research labs and big tech firms—they are now in demand across sectors—healthcare, finance, retail, manufacturing. According to the World Economic Forum, jobs regarding AI and machine learning are among the fastest growth categories; demand for these jobs is anticipated to increase by to 40% through 2027. ⁽¹⁾

The unique cross-section of skills that makes AI engineers so valuable is a hybrid of programming (Python, C++), machine learning frameworks (TensorFlow, PyTorch), data modeling, and often, backend engineering for deployment. These engineers can create models and integrate into products or systems in the real world.

With the continued integration of AI into business strategy, the capacity to evaluate and hire appropriate AI talent is becoming an important business differentiator. The hiring process is not the sole burden of tech leads and non-technical hiring managers also play an important role. Non-technical hiring managers usually assess whether candidates are aligned with company values, collaborate with others in order to work across departments and can operate in a fast-paced, cross-functional environment.

The non-technical managerial understanding of the fundamentals of the AI engineer role supports non-technical managers to ask appropriate questions that reveal potential and make balanced hiring decisions to support the entire team, not just the tech stack.

Key Technical and Non-Technical Skills to Assess

When hiring AI engineers, it’s essential to evaluate both technical capabilities and soft skills—each playing a vital role in long-term success. Here’s a breakdown of what to look for and how to assess each category effectively:

Technical Skills Assessment

Key AI Skills and How to Evaluate Them

Skill	What It Shows	How to Assess
Machine Learning Frameworks	Familiarity with tools like TensorFlow, PyTorch	Live coding, ML assignments, tool-specific challenges
Data Literacy	Ability to clean, analyze, and manipulate data	Data wrangling tasks, feature engineering problems
Problem-Solving & Analytical Thinking	Approach to solving complex ML challenges	Whiteboarding, case studies, real-world ML scenarios
Understanding AI Model Behavior	Awareness of limitations, bias, uncertainty in models	Debugging tasks, discussion on trade-offs and evaluation

Non-Technical Skills Assessment

Essential Soft Skills in AI Talent

Skill	What It Shows	How to Assess
Hunger	Curiosity, willingness to learn and improve	Look for open-source work, side projects, upskilling history
Judgment	Ability to make practical, data-driven decisions	Ask about real project decisions, scenario-based questions
Empathy	Communication and collaboration across teams	Team-fit interviews, cross-functional collaboration examples

Structuring the AI Engineer Interview Process for Non-Technical Managers

Hiring AI engineers may seem daunting to non-technical managers; but if you have the right framework to guide the process, you can run a systematic inquiry process that evaluates technical competence and fits within your company culture. The following will illustrate one way to do it:

AI Candidate Interview Stages and Evaluation Focus

Stage	Purpose	Activities	Example Questions
1. Phone Screen	- Basic technical understanding - Culture and communication fit	- 20–30 min phone interview - Assess fundamental knowledge & communication style	“What is the difference between supervised and unsupervised learning?” “Tell me about a time you worked with a non-technical stakeholder. How did you ensure alignment?”
2. Technical Assessment	- Evaluate hands-on skills - Test real-world reasoning	- Project-based task or take-home assignment - Live walkthrough with technical team	“Walk us through your solution and model trade-offs.” “How did you decide on the data preprocessing steps?”
3. Non-Technical & Behavioral Interview	- Understand past work experience - Assess problem-solving and accountability	- Use STAR method to explore challenges handled and actions taken	“Tell me about a complex ML problem you solved under tight deadlines.” “What was the outcome?”
4. Cross-Functional Fit Interview	- Evaluate collaboration skills - Ensure alignment with broader teams	- Involve product, marketing, or operations teams - Discuss collaboration style and adaptability	“How do you handle conflicting priorities between engineering and product teams?” “Give an example of successful cross-functional teamwork.”

💡 Pro Tip: Align Tasks with Business Needs

Ensure that the technical challenges reflect real business cases, like:

Optimizing a recommendation engine
Cleaning and structuring unorganized data
Explaining an ML model’s impact to executives

This helps you assess not just raw skill, but also business relevance and judgment.

Tips for Non-Technical Hiring Managers in AI Engineer Interviews

You don’t have to write code to hire AI engineers — but it pays to know what you should be listening for. Here are some tips to make sure you make informed decisions:

1. Listen for Clarity in Communication

Can the candidate explain complicated AI concepts (such as neural networks, model training) from the perspective that is easy to understand and relate to?
Strong communication is a sign of deep understanding — if they can’t explain it clearly, they might not fully understand it.

2. Focus on Problem-Solving Skills

Ask questions that are open-ended, such as: ““What do you do if you hit a data quality problem, or your model isn’t coming out accurately?”
Seek structured thinking, strategic trade-offs and the inclination to work well with others.

3. Look for Evidence of Collaboration

AI engineers almost never work alone. Ask: “Explain a time you worked with non-technical team members such as product managers or designers.”
Strong candidates will demonstrate an ability to adjust their communication style and work across departments.

4. Prioritize Learning Agility

AI is a rapidly growing space, so ask: "How do you stay on top of new tools and methods?" or “What’s the last ML technique or framework you learned?”
Good hires are lifelong learners who are (or strive to be) ahead of the curve.

By focusing on communication, real-world problem solving, collaboration, and adaptability, a non-technical hiring manager can spot AI engineers who not only are technically very strong—but also belong to the broader business.

Evaluating the Cultural Fit

Cultural fit can make or break a hire — and this is probably most true for AI engineers in fast-paced, cross-functional teams. And it's not merely a matter of coding ability; it's whether they are a fit with the values, mission, and day-to-day environment in your company. As a non-technical hiring manager, find joy in your super power of recognizing how well a candidate will integrate as a team member.

LinkedIn data suggests that 90% of recruiters disqualified candidates because of a perceived poor cultural fit—but with the right fit, collaboration and productivity can skyrocket if a match matters. ⁽²⁾ And, it pays dividends: businesses with cultural alignment experience reported turnover rates of up to 20% lower ⁽³⁾ —an incredible outcome given that turnover costs can reach 50‑200% of an employee's annual salary.

Ask questions like:

“What kind of work environment brings out your best?”
“Tell me about a time you adjusted your style to collaborate with someone very different from you.”

Listen for their alignment with core values—whether that’s innovation, continuous learning, or ethical responsibility—and probe how they handle feedback and cross-department communication. 

By integrating cultural-fit discussions into your AI engineer interviews, you'll help build teams that are not only technically strong, but also deeply cohesive, collaborative, and more likely to thrive.

Final Thoughts on Hiring AI Engineers for Non-Technical Managers

AI engineers recruitment isn’t just a process—it’s a decision that will determine your business future. As a nearly non-technical hiring manager, you can't know every algorithm or line of code but you are critical in assessing communication, sharing, cultural adaptability, and alignment with the market. Design the interview process thoughtfully, assess on both technical abilities and non-technical strengths, and partner closely with technical team leaders to tell your story and land awesome teammates who can make a difference.

In order to keep up with the competitive nature of the market today, we need to build high-performing AI teams quickly, with clarity and structure. If your organization wants to scale with the best talent, it might be worth working with partners who understand the tech and the people behind it. CodeMax Consulting gives you a personalized advantage so you can hire smarter, hire faster, and hire better with AI.

FAQs

What is an AI engineer, and how is it different from a software engineer?
An AI engineer specializes in creating machine learning models and systems that can learn from data, whereas a software engineer focuses on building general-purpose applications and handling technical infrastructure.

What should non-technical hiring managers look for in AI engineer interviews?
Non-technical hiring managers should focus on assessing both technical skills (like machine learning proficiency and data literacy) and non-technical skills (like problem-solving, communication, and cultural fit). This ensures the candidate can work effectively within the team.

How can I assess an AI engineer’s technical skills without being technical myself?
Non-technical hiring managers can rely on structured coding exercises, project-based challenges, and clear explanations of past work to assess technical competence. Collaboration with technical team members can help fill in knowledge gaps.

What are some common challenges in AI hiring, and how can I overcome them?
One challenge is evaluating complex technical skills. To overcome this, work closely with technical leads, focus on candidates' problem-solving abilities, and ask them to explain their process in simple terms to ensure they can collaborate with non-technical teams.

What are the key qualities of a strong AI engineer hire?
Strong AI engineers demonstrate a balance of technical expertise, problem-solving skills, and the ability to collaborate and communicate effectively. They also show curiosity and a drive to learn, adapt, and overcome challenges.

How can I ensure my AI engineer hires aligns with my company’s culture?
Assess cultural fit by discussing how the candidate works in team settings, manages cross-functional collaboration, and aligns with company values. Look for signs of empathy, communication skills, and teamwork in their responses.

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