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