How to Hire AI Engineers (What to Look For Beyond the Resume)

Why Standard Interviews Fail

The standard software engineering interview — LeetCode, system design, behavioral questions — was designed to evaluate a set of skills that were reasonably stable over time: data structures, algorithms, distributed systems, coding style.

AI engineering is different. The tools change every few months. The "right" approach to a problem depends heavily on which models are available, what their capabilities are, and what the cost and latency constraints are. The skill set required today is not the same skill set required eighteen months ago, and it won't be the same skill set required eighteen months from now.

Hiring for AI engineering requires evaluating a different set of attributes — ones that are harder to test with standard interview formats.

What Makes a Great AI Engineer

Systems thinking: The best AI engineers think about how AI fits into a larger system, not just whether the model produces good outputs. They consider data pipelines, evaluation infrastructure, cost monitoring, degradation handling, fallback behavior. They ask: "What happens when the model is wrong? What happens when the API is down? What happens when this scales to 100x the current volume?"

Empirical mindset: AI development is fundamentally empirical. You don't know if a change will improve things until you measure it. Great AI engineers build evaluation suites before they start iterating. They define what "better" means before they try to make things better. They're comfortable with uncertainty and skeptical of changes that haven't been measured.

Knowing when not to use AI: Counterintuitively, one of the best signals of an AI engineer's quality is their judgment about when AI is NOT the right tool. "You could use an LLM for that, but a regex would be faster, cheaper, and more reliable" is a good sign. Engineers who want to use AI for everything are often less capable than engineers who understand where AI adds genuine value.

Depth in at least one relevant area: Broad AI awareness is valuable, but the engineers who ship are usually strong in at least one area: NLP and LLMs, computer vision, ML infrastructure, data engineering, or AI evaluation. Look for genuine depth, not just familiarity across the surface.

Red Flags

Can only use one framework: If a candidate's entire AI experience is "I've built things with LangChain," that's a concern. Frameworks change. The engineer who understands the underlying APIs and can work with or without the framework is more durable.

Can't explain tradeoffs: Ask about a decision they made on a past project. If they can't articulate why they chose approach A over approach B — what the tradeoffs were, what information led to the choice, what they would do differently with hindsight — they're not thinking carefully about their work.

Obsessed with newest models: The engineer who is always asking "have you tried the new model that came out last week?" and wants to switch stacks with every release is a liability. Stability and measured improvement outperform constant churn. Be wary of candidates who mistake novelty for progress.

No evaluation culture: Ask how they've measured the quality of AI outputs in past work. If they say "I checked some examples and they looked good," that's a red flag. If they describe eval suites, metrics, regression testing, or A/B testing, that's a good sign.

The Interview Process That Works

Skip most of LeetCode: Algorithm puzzles are not relevant to most AI engineering work. One data structures question to check basic competence is sufficient. Don't optimize interview time for the skills that matter least.

Paid technical project: The most revealing evaluation is a small, bounded paid project that mirrors your actual work. Something like: "Here's a dataset and a task. Build a pipeline that classifies X with high precision. You have 4 hours and $20 of API budget. Show us what you build and how you evaluate it."

What to look for in the output:

• Did they build evaluation infrastructure or just eyeball the results?
• Did they consider edge cases?
• Did they document their choices and the tradeoffs?
• Is the code readable and well-structured?
• What questions did they ask before starting?

System design for AI: Give a realistic AI system design problem. "Design a document Q&A system for a legal firm with 10,000 documents, 500 lawyers, and a 99.9% uptime requirement." Evaluate:

• Do they think about data pipelines?
• Do they think about evaluation and quality monitoring?
• Do they think about failure modes and fallbacks?
• Do they ask about constraints (cost, latency, compliance) before diving into the solution?

Tradeoff conversation: Pick a specific AI engineering decision that doesn't have a clear right answer. "When would you use RAG vs fine-tuning?" "When would you use an agent vs a direct LLM call?" "When would you self-host a model vs use an API?" What you're evaluating: can they reason about tradeoffs clearly, or do they default to pat answers?

Sourcing Candidates

The best AI engineers are often not actively job searching. They're building things, writing about them, and contributing to open-source projects.

Where to find them:

• Hugging Face community and forums
• GitHub contributors to popular AI libraries
• Technical blog posts (Towards Data Science, Substack, personal blogs)
• Discord communities for specific AI frameworks
• AI conference communities (NeurIPS, ICML, local ML meetups)

LinkedIn is less useful for AI engineering than for most other roles because the best candidates are often less visible on traditional professional networks.

Write a job description that describes a specific technical challenge you're solving, not a list of requirements. Engineers who find the problem interesting will self-select. Engineers who don't won't. That's the right filter.

The Attributes That Don't Show Up on Resumes

Intellectual curiosity: Do they read papers? Follow research? Experiment outside of work? The field moves fast enough that the best engineers are self-teaching continuously.

Communication: AI engineers often need to explain complex, probabilistic system behavior to non-technical stakeholders. Can they explain a model failure mode in terms a product manager can act on?

Comfort with uncertainty: AI systems don't behave the way deterministic software does. Engineers who are frustrated by unpredictability and need clear right answers are poorly suited to AI work.

These attributes are hard to evaluate in standard interviews. The best signal is the paid project: watch how they handle ambiguity, whether they ask good questions, and whether they communicate their thinking clearly throughout.