cloud engineer for ML platforms

workflows and model deploymentDevelop reusable automation and platform capabilities that simplify onboarding, reduce manual work and improve the user experience for researchers and ML teamsEnable and maintain integrations between AWS services and supporting technologies such as Databricks, MLflow, Jenkins, Bitbucket, OpenShift and related platformsAct as the primary technical contact for stakeholders, translating business and research requirements into effective cloud and platform solutionsCreate and maintain technical documentation, support onboarding activities and contribute to the evaluation of new cloud and MLOps technologiesТребованияHands-on experience designing, implementing and supporting cloud infrastructure in AWS environmentsStrong knowledge of AWS services including SageMaker, IAM, networking, storage, compute services and container technologiesExperience with Infrastructure as Code and cloud automation practicesUnderstanding of cloud security, governance, compliance and access

management principlesExperience supporting machine learning, data science or MLOps platformsKnowledge of CI/CD practices and tools used for software and machine learning deliveryExperience working with technologies such as Databricks, MLflow, Jenkins, Bitbucket, OpenShift or comparable platformsAbility to troubleshoot complex technical issues and continuously improve platform reliability, performance and efficiencyStrong stakeholder management and communication skills, with the ability to work effectively across international and cross-functional teamsDegree or equivalent qualification in Information Technology, Computer Science or a related fieldУсловияHybrid role with approximately 3 days a week in the office. #J-18808-Ljbffr

ОписаниеBoehringer 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.ЗадачиDesign, maintain and continuously improve AWS-based infrastructure supporting machine learning workloads, including SageMaker, networking, IAM, storage, compute resources and model endpointsManage cloud environments through Infrastructure as Code while ensuring consistency, scalability and compliance with enterprise architecture, security and governance standardsMonitor platform performance, availability, security findings and resource utilization, proactively identifying and resolving operational issuesPlan and manage cloud capacity, including CPU, GPU, storage and networking resources, balancing business needs, platform performance and cost efficiencyBuild and support infrastructure for MLOps processes, including CI/CD pipelines, experiment tracking, model registries, automated