FREE ACCESS
5,000–10,000 jobs/day
See all jobs on JobTailor
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Tech Stack
Tools & technologiesAWSCloudDockerKubernetesNumpyPandasPythonScikit-LearnSQL
About the role
Key responsibilities & impact- 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.
Requirements
What you’ll need- 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.
Benefits
Comp & perks- 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.
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningdeep learningclassical ML modelsXGBoostLightGBMRandom Forestsreal-time inferencefeature engineeringmodel monitoringSQL
Soft Skills
collaborationevaluation of trade-offsproblem-solvinganalytical thinking