cloud engineer for ML platforms

Boehringer Ingelheim is a biopharmaceutical company active in human and animal health. It develops innovative therapies aimed at improving and extending lives in areas of high unmet medical need.

• Design, maintain and continuously improve AWS-based infrastructure supporting machine learning workloads, including SageMaker, networking, IAM, storage, compute resources and model endpoints;
• Manage cloud environments through Infrastructure as Code while ensuring consistency, scalability and compliance with enterprise architecture, security and governance standards;
• Monitor platform performance, availability, security findings and resource utilization, proactively identifying and resolving operational issues;
• Plan and manage cloud capacity, including CPU, GPU, storage and networking resources, balancing business needs, platform performance and cost efficiency;
• Build and support infrastructure for MLOps processes, including CI/CD pipelines, experiment tracking, model registries, automated workflows and model deployment;
• Develop reusable automation and platform capabilities that simplify onboarding, reduce manual work and improve the user experience for researchers and ML teams;
• Enable and maintain integrations between AWS services and supporting technologies such as Databricks, MLflow, Jenkins, Bitbucket, OpenShift and related platforms;
• Act as the primary technical contact for stakeholders, translating business and research requirements into effective cloud and platform solutions;
• Create and maintain technical documentation, support onboarding activities and contribute to the evaluation of new cloud and MLOps technologies.

• Hands-on experience designing, implementing and supporting cloud infrastructure in AWS environments;
• Strong knowledge of AWS services including SageMaker, IAM, networking, storage, compute services and container technologies;
• Experience with Infrastructure as Code and cloud automation practices;
• Understanding of cloud security, governance, compliance and access management principles;
• Experience supporting machine learning, data science or MLOps platforms;
• Knowledge of CI/CD practices and tools used for software and machine learning delivery;
• Experience working with technologies such as Databricks, MLflow, Jenkins, Bitbucket, OpenShift or comparable platforms;
• Ability to troubleshoot complex technical issues and continuously improve platform reliability, performance and efficiency;
• Strong stakeholder management and communication skills, with the ability to work effectively across international and cross-functional teams;
• Degree or equivalent qualification in Information Technology, Computer Science or a related field.

• Hybrid role with approximately 3 days a week in the office.