Senior ML Ops Engineer

📋 Description Build and maintain ML infra end-to-end: CI/CD, orchestration, automated training. Own model deployment and serving with low latency and high availability. Develop core MLOps capabilities: feature stores, model registries, monitoring. Operationalize infra for ML: Kubernetes autoscaling, GPU provisioning, self-service tools. Improve reliability and observability; define SLOs and automate uptime. Empower Data Scientists with standardized workflows to speed ML lifecycle. 🎯 Requirements Experience building and operating ML platforms in production. Docker, Kubernetes, Linux, and model serving at scale. ML lifecycle tooling: feature stores, registries, drift monitoring. Own prod systems: SLOs, observability (Prometheus, Grafana, Datadog). Production-quality Python or similar language. Modernize infra with reliability, risk, and cost focus. Own outcomes; communicate clearly with data. 🎁 Benefits Work from almost anywhere up to 20 days/year Mental health support: therapy and HeadSpace No meeting Fridays 6 weeks paid vacation + a day off for your birthday Paid parental leave Paid volunteer time