Beyond the Resume: Assessing Real AI Development Skills

I’ve been hiring AI developers for the past five years, and let me tell you something that’ll save you months of headaches: resumes lie. Not intentionally, most of the time. But when someone lists “TensorFlow, PyTorch, Deep Learning” on their CV, it could mean anything from “I completed a Coursera course” to “I built production models serving millions of users.”

The difference? Massive. And it’s costing companies big time.

Last month, I interviewed a candidate with an impressive resume. Stanford CS degree. Three years at a well-known tech company. Listed experience with transformers, computer vision, and reinforcement learning. On paper? Perfect. In reality? He couldn’t explain why you’d use batch normalization or walk me through a simple CNN architecture without stumbling.

This isn’t rare. It’s the norm.

The Portfolio Deep Dive

Here’s what actually works: forget the resume for a minute and dive straight into their portfolio. But not just any portfolio review. You need to go forensic.

I ask candidates to walk me through their most complex AI development project. Not just the results – the journey. What data did they work with? How did they handle missing values? What was their train/validation split strategy? Why did they choose that particular architecture?

Sarah, one of our best hires, spent twenty minutes explaining how she dealt with class imbalance in a medical imaging dataset. She talked about data augmentation techniques, cost-sensitive learning, and why she ultimately chose focal loss over weighted cross-entropy. That level of detail? You can’t fake it.

The red flag candidates give you surface-level answers. “We used BERT for text classification.” Okay, but which variant? How did you handle tokenization for your specific domain? What was your fine-tuning strategy? Did you freeze certain layers? Silence.

Practical Coding That Actually Matters

Standard coding interviews don’t work for AI roles. Asking someone to reverse a linked list tells you nothing about their ability to debug a training loop or optimize model inference.

Instead, I give candidates real problems. Here’s one of my favorites: “You have a model that’s overfitting. Walk me through your debugging process.” Then I watch them work through it live.

Good candidates start systematic. Check the learning curves as in SEO. Examine training vs validation loss. Look at the data for leakage. Consider regularization techniques. They think out loud, show their debugging intuition.

Bad candidates jump straight to “add dropout” without understanding what’s actually happening. Or worse, they suggest collecting more data as the first solution to everything.

I also love the “explain this weird result” exercise. I’ll show them a confusion matrix where the model performs terribly on one specific class. The best candidates don’t just suggest technical fixes – they ask about the data collection process, potential labeling errors, class distribution issues.

Jake, another strong hire, immediately asked about the temporal aspect when I showed him a time series prediction gone wrong. “Are we leaking future information? Is there a distribution shift between train and test periods?” That’s the kind of thinking you want.

The Architecture Interview

This is where you separate the real AI developers from the API users. I present a business problem and ask them to design an end-to-end ML system.

“We want to build a recommendation engine for an e-commerce platform with 100k products and 10M users. Design the system.”

Watch what they focus on. Do they immediately jump to collaborative filtering? Or do they ask about cold start problems, data sparsity, real-time vs batch requirements, scalability constraints?

The best candidates think beyond the model. They consider data pipelines, feature stores, model-serving infrastructure, monitoring, and A/B testing frameworks. They recognize that 90% of AI development is about building the surrounding systems—an area that directly shapes overall AI development cost.

Maria, our current ML lead, absolutely nailed this. She outlined a hybrid approach combining collaborative filtering with content-based features, discussed how to handle new users and products, explained her strategy for dealing with popularity bias, and even mentioned how she’d measure recommendation quality in production. That’s comprehensive thinking.

Red Flags That Never Lie

Some warning signs are universal. Candidates who can’t explain their choices. “Why did you use a random forest instead of gradient boosting?” If they can’t give you a thoughtful answer, they were probably just following a tutorial.

Another red flag: overconfidence about accuracy metrics. If someone claims their model achieved 99% accuracy without mentioning the dataset, evaluation methodology, or potential issues, they don’t understand the complexity of real-world AI.

I once had a candidate insist his recommendation system was “state-of-the-art” because it had a high click-through rate. When I asked about diversity, coverage, or long-term user satisfaction, he looked blank. That’s not understanding the business impact of AI.

The Technical Deep Dive

For senior roles, I go deeper. We’ll spend an hour diving into optimization techniques, regularization methods, or distributed training strategies. I’m not looking for textbook answers – I want to understand their intuition.

“When would you use batch normalization versus layer normalization?” The best candidates don’t just recite definitions. They talk about sequence lengths, computational efficiency, and specific use cases where they’ve made this choice.

Or my favorite: “Your model training is too slow. How do you speed it up?” Good answers cover data loading bottlenecks, mixed precision training, gradient accumulation, model parallelism, and infrastructure considerations. They understand that training efficiency is often the bottleneck in real projects.

Making the Call

After five years of this, I’ve learned that the best AI developers share certain traits. They’re curious about edge cases. They understand the business context of their technical decisions. They can communicate complex ideas simply. And they know what they don’t know.

The worst hires? Usually the ones with the most impressive resumes but shallow understanding. They’ve memorized the buzzwords but never actually built anything from scratch.

My advice? Spend less time on credentials and more time on demonstrated ability. The AI field is moving too fast for traditional hiring methods. The person who can think through problems systematically and adapt to new challenges will always outperform someone who just knows the current popular frameworks.

Because here’s the thing about AI development: the frameworks will change, the techniques will evolve, but the ability to think critically about data and models? That’s timeless.