Senior ML Infra Engineer — Hybrid, Copenhagen

WPP; København; Hybrid; DKK 523,000 - 822,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

WPP Open Intelligence in Copenhagen is seeking a Senior ML Infrastructure Engineer to design and operate scalable cloud-based systems supporting data and ML workloads. You will collaborate closely with data scientists and engineers to ensure production-grade, reliable platforms that scale with growing AI workloads.

You should bring 4+ years in infrastructure or data engineering, strong Python/SQL, and experience with cloud platforms (GCP/AWS/Azure) and ML tools.

Qualifications

• 4+ years of experience in infrastructure, platform, data engineering or large-scale software engineering.
• Strong Python and SQL proficiency.
• Experience designing and operating cloud environments (GCP, AWS, or Azure).
• Familiarity with ML frameworks such as PyTorch or TensorFlow.
• Familiarity with ML tools like MLflow or Kubeflow.
• Experience with CI/CD, version control, API design and testing.

Responsibilities

• Design, build, and maintain scalable, production-grade infrastructure for data and ML workloads in the cloud (primarily GCP).
• Collaborate with data scientists and engineers to translate prototypes into production systems.
• Design and implement data and ML platforms with reliability, scalability, observability, and operational maturity.
• Identify technical debt, bottlenecks, and inefficiencies across infrastructure components.
• Contribute to CI/CD best practices, testing, automation, and repository maintenance.
• Engage in knowledge sharing and continuous improvement across the team.

Skills

Python

SQL

Cloud platforms

CI/CD

Data pipelines

Communication

Tools

Docker

Kubernetes

MLflow

Kubeflow

Spark

Airflow

Job description

WPP Open Intelligence in Copenhagen is seeking a Senior ML Infrastructure Engineer to design and operate scalable cloud-based systems supporting data and ML workloads. You will collaborate closely with data scientists and engineers to ensure production-grade, reliable platforms that scale with growing AI workloads.

You should bring 4+ years in infrastructure or data engineering, strong Python/SQL, and experience with cloud platforms (GCP/AWS/Azure) and ML tools.