Senior ML Ops Engineer

📋 Description

• Build and maintain ML infrastructure end-to-end: extend and operate CI/CD pipelines, model
• Own model deployment and serving: define and evolve tooling for model serving with low latency and
• Develop core MLOps capabilities: feature stores, model registries, automated monitoring for
• Operationalize infrastructure for the ML team: enable Kubernetes autoscaling and GPU provisioning
• Improve platform reliability and performance: design resilient monitoring and automate for
• Empower Data Scientists through standardized workflows: build golden paths to streamline model

🎯 Requirements

• Experience building and operating ML platforms in production environments.
• Strong knowledge of containerization and orchestration (Docker, Kubernetes), Linux internals, and
• Familiarity with ML lifecycle tooling including orchestration frameworks, feature stores, model
• Experience owning production systems: define SLOs, build observability, participate in incident
• Comfort coding production-quality Python or similar language.
• Experience modernizing production infrastructure with a focus on reliability, risk, and cost.

🎁 Benefits

• Work from (almost) anywhere for up to 20 days per year.
• Mental health and well-being perks: therapy, HeadSpace, company-wide time off, no meetings on
• Paid parental leave and paid volunteer time.
• Career growth: Development Dollars, leadership development, on-demand e-learnings.
• Travel discounts, ERGs, and social/team events.
• Office in Friedrichshain, Berlin.