Machine Learning Innovator
Há 4 dias
Transforming payments is at the forefront of innovation in our field. As a Machine Learning Engineer, you will play a pivotal role in expanding access to technology and payments across dynamic markets.
Our platform connects leading global companies to over 1 billion consumers, facilitating seamless cross-border payments where it matters most.
We are a fintech company scaling rapidly across 29 countries, driven by AI-powered innovation and a commitment to inclusivity.
In the Data team, we turn information into actionable intelligence. As a Machine Learning Engineer focused on MLOps, your mission will be to design and deploy scalable systems for training, monitoring, and exposing machine learning models in production.
Key Responsibilities:- Owning system architecture for training, retraining, developing, monitoring, and exposing machine learning models;
- Designing, developing, and maintaining scalable infrastructure for ML pipelines and APIs;
- Implementing and managing CI/CD pipelines for ML model deployment, updates, and continuous monitoring;
- Collaborating with data scientists, software engineers, and cross-functional teams to ensure seamless integration of ML models into production;
- Ensuring compliance with security and privacy requirements, particularly in a financial context;
- Troubleshooting and optimizing ML production systems for performance, reliability, and scalability;
- Sharing knowledge, mentoring, and supporting team members in best practices for MLOPs, API development, and productionization of machine learning models.
- 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 a financial context;
- Excellent problem-solving skills and a passion for innovation.
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