Get AI/ML Engineers
Machine learning engineers who build real ML systems: training pipelines, model serving, forecasting, anomaly detection, and LLM integrations. Not just ChatGPT wrappers. Real ML.
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.
LATAM universities produce engineers with strong linear algebra, probability, and statistics foundations. ML work requires this depth. It's not common everywhere.
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.
UTC-3 to UTC-5 means meaningful overlap for experiment reviews, model performance discussions, and architecture decisions.
LATAM ML engineers tend to work on measurable business problems: demand forecasting, fraud detection, recommendation systems. They value results over model novelty.
Every candidate completes all five stages before you see their profile. You can also run your own technical round after our screening.
A timed test measuring analytical thinking, pattern recognition, and problem-solving clarity, independent of specific programming language knowledge.
A structured interview assessing communication style, conflict resolution, ownership mindset, and English proficiency in a professional context.
A 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.
Verification of work history, education, and identity with written consent, aligned with applicable privacy rules including LGPD where relevant.
We speak with at least two professional references who worked with the candidate in an engineering context, not personal contacts.
After our screening, you can optionally run your own technical round before making an offer.
Sample profiles
Anonymized profiles from our vetted talent pool. Actual candidates may vary.
São Paulo, Brazil
6+ years experience
Buenos Aires, Argentina
3+ years experience
Campinas, Brazil
8+ years experience
Want to see profiles matched to your specific requirements?
Request matched profilesWhat to expect
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.
Mid ML Engineer
Senior ML Engineer
Lead ML Engineer
FAQ
Tell us your ML problem and stack. We'll have a shortlist ready in 3 days.
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 AI