Senior Cloud Engineer

Boehringer Ingelheim; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

Boehringer Ingelheim in London is seeking a Senior Cloud Engineer, ML Platforms to design, build and evolve AWS-based infrastructure enabling ML across research and production lifecycles.

You will collaborate with researchers, ML engineers and platform teams, ensuring secure, scalable environments and supporting MLOps workflows from experimentation to deployment in a hybrid working model.

Qualifications

• Hands-on AWS cloud infrastructure design and support experience.
• Experience with SageMaker, IAM, networking, storage, compute and containers.
• Proficient in Infrastructure as Code and automation.
• Understanding of cloud security, governance and access management.
• Background in ML/DS or MLOps platforms.
• CI/CD practices and tooling.
• Experience with Databricks, MLflow, Jenkins, Bitbucket or OpenShift.
• Strong cross-functional collaboration and stakeholder management.
• Degree in IT/CS or related field.

Responsibilities

• Design, maintain and optimize AWS-based infrastructure for ML workloads (SageMaker, networking, IAM, storage, compute, endpoints).
• Manage cloud environments with Infrastructure as Code ensuring consistency, scalability, security and governance.
• Monitor platform performance, security findings and resource utilization; proactively resolve issues.
• Plan cloud capacity (CPU, GPU, storage, networking) balancing performance and cost.
• Build and support ML workflow infrastructure (CI/CD, experiment tracking, registries, deployment).
• Develop reusable automation to simplify onboarding and reduce manual work for researchers.
• Enable integrations between AWS and Databricks, MLflow, Jenkins, Bitbucket, OpenShift and related tech.
• Act as technical liaison translating scientific needs into cloud solutions.
• Create and maintain technical docs; support onboarding and evaluate new cloud/MLOps tech.

Skills

AWS Cloud

SageMaker

IaC

CI/CD

MLOps

Databricks

MLflow

OpenShift

Jenkins

Bitbucket

Security governance

Education

Degree in IT/CS or related field

Tools

Databricks

MLflow

Jenkins

Bitbucket

OpenShift

Job description

THE POSITION

The Senior Cloud Engineer, ML Platforms is responsible for building, maintaining and evolving the cloud infrastructure that enables machine learning activities within Computational Biology. Working closely with researchers, ML engineers and platform teams, you will ensure that AWS-based environments remain secure, scalable and reliable while supporting the full machine learning lifecycle, from experimentation and model training through deployment and production operations.

As part of this role, you will work closely with Boehringer Ingelheim's newly established AI Accelerator in London, supporting the cloud and platform capabilities behind its AI and machine learning initiatives. This is an opportunity to contribute to the infrastructure that enables cutting-edge research teams to develop, scale and deploy advanced AI solutions across a wide range of biomedical challenges.

Tasks and 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 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.

Requirements

• 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.

This is a hybrid role with approximately 3 days a week in the office.

Boehringer Ingelheim is an equal opportunity global employer who takes pride in maintaining a diverse and inclusive culture. We embrace diversity of perspectives and strive for an inclusive environment, which benefits our employees, patients, and communities. All qualified applicants will receive consideration for employment without regard to a person’s actual or perceived race, including natural hairstyles, hair texture and protective hairstyles; color; creed; religion; national origin; age; ancestry; citizenship status, marital status; gender, gender identity or expression; sexual orientation, mental, physical or intellectual disability, veteran status; pregnancy, childbirth or related medical condition; genetic information (including the refusal to submit to genetic testing) or any other class or characteristic protected by applicable law