Qualifications:

• Preferably 3 years of experience.
• Interest in the healthcare field;
• Practical experience with MLOps tools and platforms, such as Jenkins, Kubeflow, MLflow, Docker, and Kubernetes;
• Proficiency in Python as well as clean code best practices.
• Familiarity with databases and data management systems;
• Knowledge of cloud infrastructure, such as Google Cloud, AWS, or Azure;
• Ability to collaborate with multidisciplinary teams and communicate effectively with non-technical members.
• Advanced English (writing and reading);
• Experience (academic and/or professional) working with healthcare sector data;
• Some knowledge/experience with HL7/FHIR.

Differential:

• Master's degree in Computer Science, Computational Mathematics, Information Systems, Software Engineering, Computer Engineering, or related fields.
• Implement and maintain MLOps and DevOps workflows, ensuring continuous integration and continuous delivery (CI/CD) of ML models;
• Implement and optimize data pipelines for model training and inference;
• Monitor, validate, and ensure the integrity of models in production;
• Work in collaboration with data scientists to optimize model training and deployment;
• Develop tools and systems to track model performance and alert on degradations;
• Create and maintain documentation for MLOps solutions, ensuring best practices throughout the model lifecycle;
• Work in collaboration with non-technical teams, especially from the healthcare sector, to understand requirements and implement appropriate MLOps solutions;
• Assist in decision-making related to the scalability, robustness, and security of models in production.