Senior ML Ops Engineer

📋 Description Build and maintain ML infra end-to-end (CI/CD, orchestration, training pipelines). Own model deployment and serving with low latency and high availability. Develop core MLOps tools: feature stores, model registries, monitoring. Operationalize infra for ML: Kubernetes autoscaling and GPU provisioning. Improve platform reliability: observability and automated remediation. Empower Data Scientists with standardized golden workflows. 🎯 Requirements Experience building and operating ML platforms in production. Strong knowledge of Docker, Kubernetes, Linux internals, and model serving at scale. Familiarity with ML lifecycle tooling: feature stores, registries, drift monitoring. Experience owning production systems: SLOs, observability (Prometheus, Grafana, Datadog), incidents. Comfort writing production-quality Python or similar language. Ownership mindset; data-driven decisions; clear communication. 🎁 Benefits Work from almost anywhere up to 20 days/year SpringHealth therapy and HeadSpace subscription Company-wide week off; No meeting Fridays Paid parental leave; Paid volunteer time Development dollars and leadership development Office in Friedrichshain, Berlin; travel discounts; transit subsidies