Develop and Deploy ML Models: Build, train, and deploy robust machine learning models focused on card authorization optimization, dynamic routing, and intelligent retries.
Real-Time Inference Engineering: Design and maintain low-latency inference pipelines capable of scoring live payment transactions within strict millisecond SLAs.
Feature Engineering & MLOps: Collaborate with data teams to build scalable feature stores, ensuring data quality, and automate model training/deployment pipelines (CI/CD for ML).
Experimentation & Shadow Testing: Drive A/B testing and shadow deployment strategies to safely measure the real-world impact of your models on live traffic and revenue.
Model Monitoring: Define and monitor key performance metrics to detect data drift, model degradation, and anomalies in production environments.

Classical & Deep Learning Mastery: Deep practical expertise in designing and tuning high-performance classical ML models (e.g. XGBoost, LightGBM, Random Forests) as well as experience with deep learning.
Ability to rigorously evaluate the trade-offs between model complexity and inference latency as well as experience beyond standard accuracy metrics utilizing calibration curves, cost-sensitive learning, and precision-recall trade-offs.
Software Engineering & Python: Software engineering best practices, Python mastery and experience with the standard ML/Data libraries (Scikit-Learn, Pandas, Numpy) with a strong focus on writing scalable, production-ready code.
Real-Time Systems: Proven ability to build, deploy, and optimize ML models that operate under strict latency and high-throughput constraints.
MLOps Proficiency: Experience taking models from notebooks to production environments using tools like MLflow, Docker, Kubernetes, and CI/CD pipelines.
Strong SQL Proficiency: Ability to write complex queries and wrangle large-scale transactional datasets for feature extraction.
Payments Domain Knowledge (Nice to Have): Understanding of the card payment lifecycle, authorization processes, issuer behavior, 3D Secure, and network rules (Visa, Mastercard).
Cloud Infrastructure: Proven experience deploying and managing ML systems on AWS or similar, including expertise in infrastructure as code.

Hybrid working
We offer a hybrid structure with a 3 days / week on site expectation, so you can strike the balance between office and home working.
30-day holiday allowance.
Work from abroad policy, enabling employees to work remotely for up to another 30 days per year.
3,000 BRL annual budget to support your professional growth.
Leadership cafés and on-the-job training opportunities.
Life insurance, health insurance + dental plan and travel insurance.
Meal vouchers
BRL 54/ day.
Enhanced family leave.
Transportation Voucher
we will cover your costs of commute.
Gym membership contribution.
Deals & Coupon Platform for attractive discounts.
Mental Health Platform for therapy and well-being support.
SESC
education, health, culture, and recreational programs available.
Pet-friendly office.

Senior Machine Learning Engineer

Key skills