Jobs in the Netherlands, in English.

Machine learning engineer AI Platform

On-site in Rotterdam·Full-time·Added 11 days ago (last checked 9 Nov)

Erasmus MC

135 open roles

Apply

Our summary

Work mode: On-site, Rotterdam
Contract: Full-time
Languages: Dutch

Skills

This job advert was originally written in Dutch.

Requirements

What we're looking for

You will work with modern cloud and development technologies, including Azure, Databricks, MLflow, Python, Terraform, Docker, and Azure DevOps. You do not need to have experience with all of these technologies. More importantly, you have a solid technical foundation, enjoy learning new technologies, and want to contribute to the further development of the platform.

The role

Developing AI models is one thing. Deploying them safely, scalably, and reliably in daily healthcare is the real challenge.

Within Erasmus MC, we work on diverse AI applications. From predicting healthcare capacity and supporting healthcare professionals to new possibilities with generative AI. To bring these innovations into practice, we are building a central AI platform.

As a Machine Learning Engineer, you will develop the technical foundation that takes AI solutions from experiment to production. You will build the infrastructure, automation, and tools needed to reliably deploy, monitor, and manage AI models. You will work on a platform that is still very much under development. Many choices have not yet been made. This means that you are not only building the platform, but also influencing what it will look like.

You will get the chance to help shape standards, introduce new solutions, and help direct the technical development of AI within Erasmus MC. In this way, you contribute to AI solutions that not only work well technically, but are also actually applicable in healthcare.

What you'll do

Your responsibilities include, among other things:

Platform development: designing and developing a scalable environment in which AI models can be developed, tested, and deployed.

Automation and MLOps: setting up and improving CI/CD pipelines, automated deployments, and processes for managing AI models throughout their lifecycle.

Monitoring and reliability: contributing to insight into the performance, availability, and operation of AI solutions in production.

Integrations: connecting AI solutions with healthcare systems, such as HiX, and other applications within Erasmus MC.

Technical architecture: thinking along about the design of the platform, infrastructure as code, security, governance, and technical standards.

Collaboration and knowledge sharing: collaborating with development teams to bring AI solutions to production in a reproducible, maintainable, and reliable manner.

Additional information

Developing and training AI models is not the core of this position. Your focus lies on the technical environment required to successfully bring these models into practice.