How to Vet an AI Engineer Before Hiring

Introduction

Hiring an AI engineer can feel like finding a needle in a neural network. Every resume looks shiny, but how do you know who’s actually got the skills to deliver results? Vetting AI engineers isn’t just about checking their GitHub. It’s about making sure they can build, deploy, and scale real-world models. Whether you’re hiring freelance or full-time, learning how to vet an AI engineer before hiring is essential to avoid costly mismatches.

Why Vetting AI Engineers Is So Tricky (And So Important)

On paper, most AI engineers look like superheroes. Python? Check. TensorFlow? Check. Fancy ML course? Double check. But here’s the thing:
AI isn’t just code. It’s about solving real business problems with smart data science, thoughtful modeling, and scalable infrastructure.

If you skip vetting, you risk:

• Hiring someone who can’t move past experimentation.
• Wasting months (and thousands of dollars) on dead-end models.
• Creating tech debt that strangles your product later.

🤖 A great AI engineer isn’t the one who trains the biggest model. They’re the one who knows when not to.

Step 1: Define What You Actually Need

Before diving into interviews, get crystal clear on:

• Are you building from scratch or improving an existing model?
• Do you need help with data engineering, model development, or deployment?
• Is this a one-time project or ongoing work?

You’re not just hiring an “AI engineer. You’re hiring a problem solver. Different roles call for different skills:

• ML Engineer: Focused on algorithms, tuning, and modeling.
• Data Engineer: Optimizes pipelines, storage, and infrastructure.
• MLOps Engineer: Takes models to production and monitors performance.

Step 2: Review Their Portfolio Like a Product Manager

Forget the buzzwords. Look for results.

What to Look For:

• End-to-end projects: Did they take an idea to production?
• Clear problem framing: Can they explain the business challenge?
• Impact metrics: Accuracy is nice, but ROI is better.
• Tech stack clarity: Do they just name-drop libraries, or explain why they used them?

Ask for:

• GitHub repositories (with READMEs!)
• Case studies or write-ups
• Links to working demos or APIs

🛠️ Bonus tip: Run their projects. If you can’t reproduce their work, that’s a red flag.

Step 3: Assess Technical Depth (But Keep It Practical)

AI interviews shouldn’t be about who memorized the most ML algorithms.

Instead, focus on:

• Problem-solving ability: How would they clean a messy dataset? How do they deal with overfitting?
• Deployment know-how: Can they get a model into production or a cloud service?
• Tooling comfort: Do they work with PyTorch, TensorFlow, Hugging Face, or LangChain (for LLM use cases)?
• Evaluation mindset: Do they obsess over validation? Do they understand precision vs recall?

Use:

• Take-home tasks (realistic and time-boxed)
• Pair coding sessions on Jupyter or Colab
• Scenario-based questions

🎯 Pro Tip: Skip whiteboard theory. Give them a messy CSV and a user story instead.

Step 4: Don’t Ignore Soft Skills (Especially for Startups)

Even the most brilliant AI engineer won’t help if they can’t work with your team or understand your goals.

Soft skills to vet:

• Communication: Can they explain technical things to non-technical people?
• Curiosity: Do they ask questions before jumping to solutions?
• Autonomy: Are they proactive or do they need constant direction?
• Business sense: Can they prioritize model performance based on ROI?

Ask:

• “How did your last model impact the business?”
• “Tell me about a time you changed direction mid-project.”

Step 5: Use Vetted Platforms (When in Doubt)

If this all sounds overwhelming, it can be. Especially if your team lacks technical hiring experience.

That’s where niche platforms like Loopp come in:

• All engineers are pre-vetted for AI-specific skills.
• You can view portfolios with real-world results.
• Plus, Loopp offers technical PMs to keep your AI project on track.

Using vetted marketplaces reduces the risk of hiring someone who talks the talk but can’t build the model.

Common Mistakes to Avoid

1. Relying on resumes alone: AI talent often comes from non-traditional backgrounds.
2. Overvaluing academic experience: PhDs are great but not always production-ready.
3. Ignoring deployment experience: If they’ve never pushed a model live, beware.
4. Rushing the process: Take your time to vet. It’s faster than cleaning up later.

Conclusion: Vet Before You Regret

Hiring the right AI engineer can unlock incredible value for your startup but only if you vet them properly. Skip the fluff, dig into real projects, ask sharp questions, and don’t settle for surface-level skills. Whether you’re hiring freelance or full-time, a well-vetted AI engineer is the difference between model success and mess.

Want to skip the vetting headache? Check out Loopp and hire pre-vetted AI engineers who’ve been tested, not just interviewed.