As a Cloud (ML)Ops Engineer, you’ll work at the intersection of cloud infrastructure, DevOps, and machine learning operations. Together with your team, you’ll help build a reliable, scalable, and secure platform that supports data scientists and analysts throughout their entire workflow.

This includes

• Hosting a multi-user Jupyter environment and a cloud IDE;

• Providing frameworks for training, storing, serving, and monitoring custom models, primarily for high throughput batch processing;

• Exposing models via APIs for low latency request-response use cases;

• Enabling Generative AI initiatives.

Responsibilities

• Designing and building cloud-native platform services for AI models and data pipelines.

• Collaborating with colleagues and stakeholders across countries to develop technical solutions.

• Managing infrastructure using tools like Terraform, Docker, and Kubernetes on AWS.

• Automating workflows for data processing and model lifecycle management (Airflow, Spark, and Python)

• Ensuring platform reliability, performance, and cost-efficiency.

• Supporting colleagues in using the platform, including onboarding and troubleshooting.

• Contributing to the evolution of our MLOps practices.

Ideal Profile

• You have a strong interest in cloud, data and AI, and eager to learn about new developments in the field.

• Education or experience: Master’s degree in ICT, Engineering Sciences or Business Engineering with a focus on Informatics, or equivalent experience.

Technical skills

• Proficient in Python and the broader data science ecosystem.

• Experience with cloud infrastructure (preferably AWS).

• Familiar with Docker and Kubernetes.

• Skilled in infrastructure as code (Terraform).

• Experience with CI/CD tools like Jenkins or GitHub Actions.

• Knowledge of big data tools such as Spark.