ML Platform Engineer

Key Responsibilities

• Build and maintain scalable AWS infrastructure for machine-learning workloads using Terraform.
• Develop reliable training and inference pipelines with Apache Airflow, and model deployment and monitoring workflows with MLflow.
• Improve the tooling used by data scientists and ML engineers throughout the model lifecycle.
• Automate model deployment, testing and infrastructure provisioning with GitHub Actions and CI/CD.
• Run containerised workloads with Docker and orchestrate them on Kubernetes.
• Improve platform reliability, observability, scalability and cost efficiency.
• Work closely with ML, data and software engineers to understand platform requirements.
• Establish repeatable processes for production machine-learning operations (MLOps).

Technical Skills

• 2+ years of professional ML platform, MLOps or machine learning infrastructure experience.
• Strong Python and software-engineering fundamentals.
• Experience with Docker and container orchestration on Kubernetes.
• Hands-on experience with AWS cloud infrastructure and infrastructure as code using Terraform.
• Experience with MLflow for experiment and model tracking, and Apache Airflow for pipeline orchestration.
• Experience with Git, GitHub Actions and CI/CD.
• Experience with PostgreSQL, MongoDB and Redis.
• Strong MLOps practice, including machine learning model deployment and model monitoring.
• Experience building training/inference pipelines and production ML systems.

Language Requirements

• German: B2 (Upper-Intermediate) (required for this position)
• English: C1 (Advanced)

Assessment

Test Task Required

Benefits

• Fully remote position within Germany.
• 28 days paid vacation.
• Home-office equipment allowance.
• Annual learning and development budget.