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