The generative AI revolution has created explosive demand for a new type of software engineer. It's no longer enough to hire someone who can plug in an API. Companies need engineers who can build, fine-tune, and deploy complex machine learning systems.
The problem is that these skills are new, rare, and hard to assess. A resume that says "AI" doesn't mean much without a process to vet what that actually looks like in practice.
This guide outlines what to look for and how to hire engineers with true AI fluency. The goal is to help you build a high-performance team that can execute in one of the most technically demanding domains in modern software.
What to look for: The core competencies of an AI engineer
Here’s how to assess for real-world skill beyond the resume.
1. Foundational machine learning concepts
- What it means: Deep understanding of model types, training strategies, and evaluation metrics
- How to assess: Ask open-ended questions in a conceptual interview. For example, “How would you build a model to detect fraudulent transactions?”
2. Practical experience with AI frameworks
- What it means: Proficiency with Python and libraries like PyTorch, TensorFlow, Hugging Face
- How to assess: Run a live coding session where the candidate fine-tunes a small language model or implements a simple neural net
3. Data engineering and MLOps skills
- What it means: Ability to build data pipelines and manage cloud infrastructure for deploying models
- How to assess: Use a system design interview to have them architect an end-to-end ML pipeline
4. Problem-solving and critical thinking
- What it means: Clear thinking about trade-offs between accuracy, speed, and cost in solving business problems
- How to assess: Present a case study and evaluate how they reason through real-world deployment decisions
The Howdy.com AI vetting process
We’ve built a dedicated vetting track for AI engineers, designed and led by experienced practitioners in the field.
AI-focused technical interviews
- Includes tasks like fine-tuning open-source models, working with vector databases, and implementing ML algorithms from scratch
AI-specific system design questions
- Candidates might be asked to architect a Retrieval-Augmented Generation (RAG) pipeline or design a real-time recommendation engine
AI-enabled talent pool
- We proactively build and certify our network of engineers across Latin America through internal programs like Howdy’s AI Certification, helping them stay at the frontier of the field.
Key roles to hire for your AI team
To launch or scale an AI function, these are the key roles to prioritize:
- Machine Learning Engineer: Designs, trains, and deploys models
- Data Engineer: Builds and maintains the data infrastructure for model development
- MLOps Engineer: Owns the pipelines and tooling for scalable, reliable model deployment
- AI Research Scientist: Explores new models and algorithms to keep your capabilities at the cutting edge
Conclusion: You can’t fake AI expertise
Many candidates say they’ve worked with AI. Few can prove they’ve built something meaningful with it.
The only way to build a capable AI team is to use a rigorous, human-led vetting process that digs into the real skills. That means live coding, architectural thinking, and deep conversations with experienced engineers who know what good looks like.
Howdy.com makes that process seamless. We connect you with the top 1% of vetted AI engineers from Latin America — talent that has already demonstrated their ability to build production-grade AI systems.
When the future of your product depends on getting AI right, the quality of your team is everything. We make sure you hire the best.