Senior MLOps Engineer

📋 Description

• Build and maintain ML infrastructure end-to-end (CI/CD, model orchestration, automated training
• Own model deployment and serving for low latency and high availability
• Develop core MLOps capabilities (feature stores, model registries, monitoring for performance &
• Operationalize infrastructure with Kubernetes autoscaling and GPU provisioning; manage a
• Improve platform reliability and performance with observability and SLO-driven automation
• Empower Data Scientists with standardized, self-service workflows for the ML lifecycle

🎯 Requirements

• Experience building and operating ML platforms in production
• Docker, Kubernetes, Linux internals, and scalable model serving
• Familiarity with ML lifecycle tooling (orchestration frameworks, feature stores, model registries
• Experience owning production systems, observability (Prometheus, Grafana, Datadog), incident
• Production-quality Python or similar language
• Reliability-focused approach to modernizing production infrastructure

🎁 Benefits

• Work from Berlin office with hybrid model (3x/week in office)
• Comprehensive tech and wellbeing perks including mental health resources, learning allowances, and
• Flexible benefits surrounding travel, parental leave, volunteer time, and career development