Machine Learning Engineering Specialist

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.

Must-haves to shine in this role:

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.

Bonus points if you have:

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.