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.

• Hands-on experience as a Machine Learning Engineer, MLOps Engineer, or in related roles, with solid practical experience building and supporting infrastructure for machine learning models in production;
• Strong experience with cloud platforms such as AWS, GCP, or Azure;
• Proficiency with containerization tools such as Docker and orchestration systems (e.g., Kubernetes);
• Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and the infrastructure and production requirements of ML models;
• Strong Python skills with experience writing clean, efficient, and maintainable code;
• Experience with CI/CD tools such as GitLab CI, Jenkins, or GitHub Actions, as well as infrastructure as code tools such as Terraform;
• Knowledge of data pipelines and ETL processes (streaming and batch);
• Experience with monitoring, logging, and alerting solutions for machine learning services (e.g., Prometheus, Grafana, Datadog);
• Understanding of security and compliance standards in payment systems.
• Conversational English skills, with the ability to communicate effectively with international stakeholders, participate in discussions, and occasionally present or speak in English.

• Knowledge of Kafka or similar event streaming platforms;
• Familiarity with financial services or payment systems;
• Experience working with products that use machine learning or AI in their core;
• Experience with risk-related products, transaction routing, fraud prevention, or other payment use cases.