About the role
ML Ops Engineer at a well-funded distributed-cloud infrastructure startup, hired through Pragmatike.
The company delivers GPU-powered, AI-native cloud services (compute, secure storage, high-speed transfer) on a decentralized, lower-impact architecture. This role owns the MLOps layer: the pipelines, automation and tooling that take models from development into reliable production on that infrastructure. You would build reproducible training and deployment workflows, plus monitoring and lifecycle management for models running at scale. It suits an engineer who lives at the intersection of ML and platform/infra.
Highlights
- Own the MLOps layer: pipelines, automation and tooling, plus reproducible training and deployment workflows
- Monitoring and lifecycle management for models running at scale, for an engineer at the ML and platform/infra intersection
- Fully remote within EMEA timezones, English required
- Differentiator: MLOps on top of a novel decentralized GPU cloud rather than a standard hyperscaler
This recap is dataskew's editorial summary, not the company's copy.