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About the Role
As a Senior Cloud Engineer for ML Platforms, you will build and evolve the cloud infrastructure that enables machine learning activities for Boehringer Ingelheim's newly established AI Accelerator in London. You will ensure that AWS-based environments remain secure, scalable and reliable while supporting the full machine learning lifecycle. You will join at an early stage with real influence on how the platform is shaped, helping cutting-edge research teams develop, scale and deploy advanced AI solutions.
Responsibilities
- 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, ensuring consistency, scalability and compliance with enterprise architecture, security and governance standards.
- Monitor platform performance, availability, security findings and resource utilisation, 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, CI/CD pipelines and related platforms.
- Partner closely with 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.
Requirements
- Hands-on experience designing, implementing and supporting cloud infrastructure in AWS environments.
- Proven experience building and operating cloud infrastructure for ML/MLOps platforms – e.g. model training environments, ML pipelines, model registries or model deployment and serving.
- 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.
- Knowledge of CI/CD practices and tools used for software and machine learning delivery.
- 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.
Skills
- AWS
- SageMaker
- IAM
- Networking
- Storage
- Compute services
- Container technologies
- Infrastructure as Code
- Cloud automation
- Cloud security
- Governance
- Compliance
- Access management
- CI/CD practices
- MLOps
- Databricks
- MLflow
Location
- London
Work Type
- Full-time
Experience Level
- Senior
Education Level
- Degree in Information Technology, Computer Science or a related field, or equivalent practical experience.
About the Company
- Boehringer Ingelheim has been recognised as a Top Employer in the UK, demonstrating our commitment to building an exceptional workplace through strong people practices and supportive HR policies.