Evaluating AI Engineers from LATAM requires a structured, modern approach. Traditional coding tests or generic interviews are not enough for roles involving LLMs, RAG systems, MLOps, and production-grade ML. This guide details exactly how US startups should test, evaluate, and select the best AI talent.
What US Startups Should Look for in AI Engineers?
Core ML skills
- Python
- TensorFlow / PyTorch
- Data preprocessing, model training
- Classical ML & deep learning fundamentals
LLM & generative AI skills
- RAG pipeline construction
- Vector databases & embeddings
- Prompt engineering
- LangChain / custom orchestration frameworks
- Fine-tuning small and large models
Production readiness
- Deployment on AWS/GCP/Azure/Modal
- Model monitoring + evaluation
- CI/CD for ML systems
Communication skills
Top AI Engineers can explain complex architectures clearly to product and leadership teams.
The Best Technical Tests for Evaluating AI Engineers
1. Python ML Coding Test (Baseline)
Should evaluate:
- Vectorized operations
- Model-building reasoning
- Clean code and data handling
2. LLM-Oriented Tasks (Must-Have in 2025)
Ideal tests include:
- Building a basic RAG pipeline
- Implementing embeddings with FAISS or other vector DBs
- Evaluating prompt strategies
- Designing an LLM agent with structured tools
3. Applied ML Challenge (End-to-End)
A small, paid challenge should measure:
- Data cleaning
- Feature engineering
- Model selection
- Deployment approach
- Monitoring and evaluation
4. Architecture Discussion
Ask them to explain:
- Their most significant ML/LLM system
- How they handled scaling
- How they balanced latency, inference cost, and accuracy
How to Score AI Engineers Effectively
Technical score (50%)
Python, ML stack, LLM work, MLOps.
Problem-solving score (25%)
Can they improve an existing system? Can they debug and optimize a model?
Communication score (15%)
Clarity matters in AI development cycles.
Product mindset (10%)
Top AI Engineers think “feature impact,” not just “model accuracy.”
Evaluating Engineers from Different LATAM Regions
LATAM is diverse, and candidate profiles vary slightly by country.
Brazil
Strong deep learning & research backgrounds.
Mexico
Excellent production engineers with strong backend collaboration.
Colombia / Argentina / Chile
High English proficiency and strong LLM adoption.
🚀 Book a Free Discovery Call to Hire Your Next AI Engineer.
Why Most Startups Rely on Vetted Platforms for Evaluations?
Simera (Best for AI Engineering Roles)
Simera is an AI-powered global talent platform sourcing AI Engineers from LATAM, the Middle East, and Southeast Asia.
Provides:
- Advanced ML + LLM vetting
- Python/ML assessments
- Communication and cultural screening
- Shortlists in 72 hours
Interfell
Covers LATAM and Spain. Good for general tech roles; limited AI-specific evaluation.
Other platforms (minimal detail)
- Upwork basic tests only
- Fiverr not suitable for AI engineering
- Job boards no technical vetting
💼 Hire Pre-Vetted AI Engineer Professionals from Our Talent Pool.
FAQs
Q1: What is the most important skill to test in an AI Engineer?
Python + applied ML/LLM problem solving.
Q2: Should I test LLM skills even if the role is ML-focused?
Yes LLM literacy is now a core expectation for AI roles.
Q3: How long should a technical challenge be?
2–4 hours is ideal; avoid overly long assignments.
Q4: How do I know if an engineer has real production experience?
Ask for architecture explanations, model deployment examples, and monitoring strategies.
Properly evaluating AI Engineers requires more than generic coding tasks. US startups that leverage structured ML/LLM assessments, communication interviews, and production-based evaluations consistently hire stronger, more reliable LATAM talent.
