MLOps Engineer

Are you passionate about cloud infrastructure, DevOps, and machine learning? As a Cloud (ML)Ops Engineer, you’ll help build a reliable, scalable, and secure platform that empowers data scientists and analysts to bring AI ideas to life.

What you’ll do:

✨ Host a multi-user Jupyter environment and a cloud IDE

✨ Build frameworks for training, storing, serving, and monitoring models

✨ Expose models via APIs for low-latency applications

✨ Enable Generative AI initiatives across the organization

Your mission includes:

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

• Collaborating with colleagues across countries to deliver cutting-edge solutions

• Managing infrastructure with Terraform, Docker, and Kubernetes on AWS

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

• Ensuring platform reliability, performance, and cost-efficiency

• Supporting colleagues in platform usage, including onboarding and troubleshooting

• Driving the evolution of MLOps practices

What we’re looking for:

You’re curious about cloud, data, and AI, and excited to learn and innovate.

Education & Experience:

🎓 Master’s degree in ICT, Engineering, Business Engineering with Informatics focus, or equivalent experience

Technical Skills:

• Strong Python skills and familiarity with the data science ecosystem

• Experience with cloud infrastructure (AWS preferred)

• Proficiency with Docker & Kubernetes

• Skilled in Infrastructure as Code (Terraform)

• Experience with CI/CD tools (Jenkins, GitHub Actions)

• Knowledge of big data tools such as Spark

If you’re ready to take AI to the next level and work in a dynamic, innovative environment, this is your chance! 💡