Top 5 Skills to Look for in an AI Engineer

So, you’re ready to hire an AI engineer. Great! But let’s be honest, between the resumes packed with buzzwords and the LinkedIn profiles shouting “AI wizard”, how do you actually know who’s the real deal?

Hiring AI talent isn’t about finding someone who can just say “neural networks” confidently or run a prebuilt notebook. It’s about identifying the blend of coding skill, analytical thinking, and problem-solving mindset that turns abstract ideas into working intelligence. Below are the five key skills to look for in an AI engineer, explained in plain English, no hype included.

1. Strong Programming Foundations

Yes, Python is the reigning monarch in the AI world, but it’s not the only language that matters. The right engineer should be fluent in multiple languages and know when to use each.

A solid AI engineer should master:

• Python: For machine learning libraries like TensorFlow, PyTorch, and scikit-learn.
• SQL: For querying and managing large datasets efficiently.
• C++ or Java: For building high-performance models and scalable backend systems.

It’s not just about familiarity, it’s about writing clean, reusable, and optimized code. Poorly structured scripts can cripple model performance and make scaling a nightmare later.

Pro tip for hiring: Ask to see their GitHub or code samples. Bonus points if they’ve contributed to open-source AI projects.

Why it matters: AI is only as strong as the code behind it. Well-written, maintainable code is essential for debugging, collaboration, and scaling your AI systems effectively.

2. Mastery in Machine Learning and Deep Learning

This is the core of any AI engineer’s expertise. A hire-ready engineer won’t just know what machine learning is—they’ll know how to apply it strategically.

They should understand:

• Supervised vs. unsupervised learning
• Model training, evaluation, and fine-tuning
• Deep learning architectures like CNNs, RNNs, and Transformers
• Reinforcement and transfer learning

Hands-on experience should include:

• Frameworks like scikit-learn, XGBoost, Keras, PyTorch, or TensorFlow
• Building, testing, and optimizing models for specific business goals

Why it matters: The difference between theoretical knowledge and applied skill is everything. It’s one thing to describe a decision tree, it’s another to build one that detects fraud or predicts customer churn at scale.

3. Data Wrangling Like a Pro

Even the most advanced AI model is useless without quality data. A great AI engineer is part detective, part surgeon when handling datasets.

They should know how to:

• Handle missing values and outliers
• Normalize, encode, and transform data
• Work with both structured (tables) and unstructured (text, images, audio) data
• Create efficient preprocessing pipelines using tools like Pandas or Spark

Fun fact: Over 70% of an AI engineer’s time is often spent cleaning and preparing data—not building models.

Why it matters: Garbage in, garbage out. A model trained on messy data will produce unreliable, biased, or even dangerous outputs. Good engineers prevent that before it starts.

4. Deployment Skills Because Models Don’t Just Sit on Laptops

Building a great model is one thing. Getting it to run in the real world, at scale, is another. That’s where deployment skills come in.

An AI engineer should know how to:

• Version, test, and containerize models
• Deploy using REST APIs or frameworks like Flask and FastAPI
• Utilize cloud services like AWS SageMaker, Azure ML, or Google Vertex AI
• Manage CI/CD pipelines and monitoring tools for continuous improvement

Red flag: If they’ve only ever built models in Jupyter notebooks, they might be more academic than production-ready.

Why it matters: The most accurate model on a laptop won’t generate value until it’s serving real users. Deployment bridges the gap between experimentation and business impact.

5. Communication and Problem-Solving Mindset

Surprise—great AI engineers aren’t just technical. They’re communicators, collaborators, and critical thinkers.

They need to:

• Explain model behavior to non-technical stakeholders
• Translate business challenges into data-driven solutions
• Work seamlessly across teams—Product, Design, and Engineering

Ask questions like:

• “Can you describe your last AI project to a non-technical audience?”
• “How did your model impact the business outcome?”
• “What would you do differently next time?”

Why it matters: You’re not just hiring someone to code. You’re hiring someone who can solve problems, communicate ideas clearly, and align technology with strategy.

Conclusion

Hiring an AI engineer shouldn’t feel like deciphering a PhD thesis. Focus on these five skills, programming, ML mastery, data wrangling, deployment, and communication, and you’ll dramatically improve your odds of hiring someone who builds results, not just models.

Whether you’re launching an MVP or scaling enterprise AI, look beyond the buzzwords. Choose engineers who blend technical excellence with business understanding and accountability.

If you’re ready to skip the guesswork, Loopp connects you with pre-vetted, world-class AI engineers who already check every box. Because building great AI starts with hiring great people.