Get AI/ML Engineers
Get AI/ML Engineers from Latin America
Machine learning engineers who build real ML systems: training pipelines, model serving, forecasting, anomaly detection, and LLM integrations. Not just ChatGPT wrappers. Real ML.
Why LATAM for AI/ML engineering
Strong cost advantage on a scarce skill
Senior ML engineers in LATAM cost $80-110k vs $200k+ in the US. ML expertise is rare globally, making LATAM especially valuable as a source.
Strong mathematics and statistics education
LATAM universities produce engineers with strong linear algebra, probability, and statistics foundations. ML work requires this depth. It's not common everywhere.
Data-rich industry experience
Brazil's fintech, agritech, and logistics sectors have driven real ML adoption. Engineers here have built forecasting and anomaly detection systems on real production data.
Timezone for research and development
UTC-3 to UTC-5 means meaningful overlap for experiment reviews, model performance discussions, and architecture decisions.
Practical ML, not hype
LATAM ML engineers tend to work on measurable business problems: demand forecasting, fraud detection, recommendation systems. They value results over model novelty.
What our AI/ML engineers know
Python 3.12+
TensorFlow 2.x
PyTorch 2.x
scikit-learn
Airflow / Prefect / Dagster
FastAPI for model serving
Docker + Kubernetes for ML workloads
Prompt engineering and evaluation
pandas / NumPy / Polars
Jupyter Notebooks
AWS SageMaker / GCP Vertex AIHow we vet AI/ML engineers
Every candidate completes all seven stages before you see their profile. You can also run your own technical round after our screening.
* For common roles like full-stack. Other roles typically take 7 to 10 days.
CV check
Stage 1We review experience, stack, seniority, and career history against the role before anyone is invited to interview. Most applicants stop here.
Behavioral interview
Stage 2A structured interview assessing communication style, conflict resolution, ownership mindset, and English proficiency in a professional context.
Technical interview 1
Role-specificA 90-minute session covering model evaluation: candidates explain how they'd choose between evaluation metrics for a given problem (precision vs recall vs F1 vs AUC), how they'd detect and prevent data leakage, and how they'd diagnose a model that performs well on validation but degrades in production. Candidates also walk through an MLOps scenario: how would you deploy and monitor a forecasting model that retrains weekly? We assess their understanding of the full ML lifecycle, not just the modeling step.
Technical interview 2
Stage 4A second, deeper round on system design, architecture tradeoffs, and code review, matched to the seniority you are hiring for.
Take-home assessment
Stage 5A practical assignment that mirrors real work. We review code quality, testing, documentation, and how the candidate explains their decisions.
Reference check
Stage 6We speak with at least two professional references who worked with the candidate, not personal contacts.
Background check
Stage 7Verification of work history, education, and identity with written consent, aligned with applicable privacy rules including LGPD where relevant.
After our screening, you can optionally run your own technical round before making an offer.
Sample profiles
Who you might hire
Anonymized profiles from our vetted talent pool. Actual candidates may vary.
Senior ML Engineer
São Paulo, Brazil
6+ years experience
Mid ML Engineer
Buenos Aires, Argentina
3+ years experience
Lead ML Engineer
Campinas, Brazil
8+ years experience
Want to see profiles matched to your specific requirements?
Request matched profiles
What to expect
What sets our AI/ML engineers apart
ML engineers who only know how to call the OpenAI API are common. Ours understand the full spectrum: when a linear regression is the right model, how to evaluate a forecasting system in production, and when LLM integration actually solves the problem vs when it creates a new one.
They use AI tools (Cursor, Copilot) for Python boilerplate, data transformation code, and test generation. They apply their time to model architecture and experiment design.
AI-assisted experiment tracking: using AI tools to summarize experiment results and identify patterns across runs in Weights & Biases or MLflow.
They write experiment documentation that someone else can reproduce. Notebooks are code, and they treat them that way.
They communicate model performance in business terms. Not just 'the AUC improved by 0.03' but what that means for false positive rate and customer impact.
They build monitoring into model serving. Data drift detection, prediction distribution tracking, and alerting on model degradation are part of their standard deployment.
LATAM vs US AI/ML engineer salaries
Mid ML Engineer
Senior ML Engineer
Lead ML Engineer
FAQ
Common questions
Ready to meet your AI/ML engineers?
Tell us your ML problem and stack. We'll have a shortlist ready in 3 days.