Machine Learning Engineering Specialist

EBANX is one of the most successful fintechs to emerge from Latin America — and today, we are building a truly global payments company. Our mission has remained constant from day one: to unlock access and enable companies and consumers to participate in the digital economy, no matter where they are.

What started as a bold vision has grown into a platform that connects some of the world’s largest digital businesses with customers across 21 of the fastest-growing markets. We operate where complexity exists — turning local challenges into global opportunities, and building the infrastructure that allows payments to move further, faster, and smarter.

We are a team of builders and problem-solvers. We think globally, act with curiosity, and believe diversity of thought is a competitive advantage.

As EBANX enters its next phase of hyper growth, we are looking for people who want to shape the future of payments, expand what’s possible, and help connect businesses and consumers across borders.
Let’s build what’s next — together.

At EBANX’s Data team you don’t just work with numbers. You transform information into intelligence that drives strategic decisions and directly impacts the digital payments revolution on a global scale. Every analysis combines curiosity, rigor, and collaboration, making our work truly Out of the Ordinary.

• As an AI Engineer Specialist your mission will be to own the design and deployment of scalable AI systems in production.

Your day-to-day impact:

• Responsible for designing the system architecture to train, retrain, develop, monitor, deploy, and serve machine learning models in production;
• Design, develop, and maintain scalable infrastructure to support machine learning pipelines, APIs, and ML products;
• Implement and manage CI/CD pipelines for the deployment, updating, and continuous monitoring of machine learning models and services;
• Work with infrastructure and engineering practices specifically designed for machine learning products, including deployment, APIs, automation, and infrastructure as code;
• Collaborate closely with data scientists, software engineers, and cross-functional teams to ensure seamless integration of machine learning models into production;
• Ensure compliance with security and privacy requirements, especially in the financial context;
• Troubleshoot and optimize machine learning production systems, ensuring performance, reliability, scalability, and operational stability;
• Share knowledge, mentor, and support team members in MLOps best practices, API development, and machine learning model production.