A great ML engineer is 20 percent model and 80 percent production. We place senior ML engineers who own data pipelines, training, deployment, monitoring, and the feedback loops that keep models honest in the real world.
Models that ship, serve real traffic, retrain on fresh data, and have fallback plans when something breaks.
Feature stores, model registries, CI/CD for models, drift monitoring, retraining pipelines. Not a Jupyter notebook.
Models are only as good as the data. Senior ML engineers own the pipeline from ingestion through feature engineering through serving.
Machine Learning Engineers
Brazil, Chile and Argentina have deep ML communities, trained at MercadoLibre, Nubank, Rappi, NotCo, and university programs that have been running for decades (ITBA, UNICAMP, PUC Chile).
3-5 people who fit the exact role, not a résumé dump.
We screen ML engineers on a real end-to-end task: data preparation, model training, evaluation, and a deployment plan.
Not a fit? We replace at no cost.
Operators who’ve made this hire, not account managers.
We start with a 30-minute call. You tell me the role, the stack, and what “great” looks like. No charge, no obligation.
We screen against the role-specific bar and send 3-5 people in days, not a résumé dump.
Meet your favorites. We coordinate everything. Most clients hire within ~3 weeks of the first call.
We handle contracts and payroll via Talent Management (COR). Not a fit in 90 days? We replace at no cost.
ML hiring is where the gap between academic ability and production ability is widest. Four traps founders fall into.
Kaggle rewards model performance on a static dataset. Production rewards robustness, monitoring, and rollback plans. The best ML engineers we place have shipped models that failed in production and lived to redesign them.
A model that cannot be retrained, monitored, or rolled back is a liability. If a candidate cannot describe their MLOps approach end-to-end, they are not senior.
Models are only as good as the data pipeline. ML engineers who cannot own the data flow leave you with a model nobody can retrain. Screen for SQL, dbt, and warehouse fluency.
ML engineers train and deploy models. <a href='./hire-ai-engineers-latin-america.html'>AI engineers</a> build products on top of existing models. Both are senior roles, but they screen for different things. Match the hire to the problem.
Data scientists prototype and analyze. ML engineers productionize and operate. If you need models that serve real traffic reliably, hire an ML engineer. If you need research and experimentation, hire a data scientist. Many senior people blur the line, we screen for the production side.
LLM engineers build on top of existing models (OpenAI, Anthropic, open-weights). ML engineers train and deploy custom models. If your product needs a bespoke model (classification, recommendation, computer vision), hire an ML engineer. If your product is primarily a GenAI feature, hire an LLM engineer.
Yes. Senior ML engineers are expected to own MLOps end to end: feature stores, model registries, CI/CD, monitoring, drift detection, retraining. If a candidate cannot explain their MLOps approach, they are not senior.
PyTorch, TensorFlow, scikit-learn, XGBoost, LightGBM for modeling. MLflow, Kubeflow, Airflow, dbt for ops. Framework fluency matters less than architecture fluency, we screen for both.
Yes, that is where senior ML engineers earn their keep. Feature engineering, data quality, and label noise are the real problems. Model choice is usually the easy part.
Hire when you want a dedicated engineer on your team long-term; use our AI Development service when you want us to build it with you. We will help you decide on the call.
Typically 50% below US rates for equivalent seniority. See the Compensation Report or book a call for specifics.
Yes. Direct-hire, contract-to-hire, or embedded contractor, we structure around how you want to work. Start with a call.
“Tell me what you are trying to model. I will send you ML engineers who have already shipped something like it, with the monitoring to prove it.” Calvin, CEO of Awana