Machine Learning Engineering Specialist

2 semanas atrás

Blumenau, Santa Catarina, Brasil EBANX Tempo inteiro

At EBANX, you'll help expand access to payments and technology in some of the world's most dynamic markets. We're a unicorn-status fintech , AI-powered , and scaling fast across 29 countries and counting .

Our platform connects leading global companies to more than 1 billion consumers , enabling seamless cross-border payments where it matters most. We build with purpose, move with speed, and create solutions that are both innovative and inclusive.

If you're looking to be part of a company that's transforming the future of payments with clarity , ambition , and real-world impact — we'd love to meet you.

In the Data team, we turn information into intelligence. We model, organize, and analyze data to generate strategic insights that drive decision-making across EBANX. We're naturally curious and guided by evidence. As aData Scientist Specialist focused onMLOPs your mission will be to own the design and deployment of scalable systems to train, monitor, and expose machine learning models in production.

  • Owning the design of system architecture to train, retrain, develop, monitor, and expose machine learning models;
  • Design, develop, and maintain scalable infrastructure for ML pipelines and APIs;
  • Implement and manage CI/CD pipelines for ML model deployment, updates, and continuous monitoring;
  • Collaborate closely with data scientists, software engineers, and cross-functional teams to ensure seamless integration of ML models into production;
  • Ensure compliance with security and privacy requirements, particularly in a financial context;
  • Troubleshoot and optimize ML production systems, ensuring performance, reliability, and scalability;
  • Share knowledge, mentor, and support team members in best practices for MLOps, API development, and productionization of machine learning models.
Must-haves to shine in this role:
  • Experience in MLOps, DevOps, or related roles;
  • Strong experience with cloud platforms (AWS, GCP, Azure);
  • Proficiency in containerization tools like Docker and orchestration systems (e.g., Kubernetes);
  • Familiarity with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn);
  • Strong Python skills with experience in writing clean, efficient, and maintainable code;
  • Experience with CI/CD tools (e.g., Jenkins, GitLab CI, GitHub Actions);
  • Knowledge of data pipelines and ETL processes (streaming and batch);
  • Experience in monitoring, logging, and alerting solutions for ML services (e.g., Prometheus, Grafana, DataDog);
  • Understanding of security and compliance standards in

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