Staff MLOps Engineer

Staff MLOps Engineer focuses on build the shared ai/ml platform: training infrastructure, experiment tracking, model registry, serving, monitoring. built once, used by synthetic, trust score, and whatever comes n.

What the role involves

• Build the shared AI/ML platform: Training infrastructure, experiment tracking, model registry, serving, monitoring. Built once, used by Synthetic, Trust Score, and whatever comes n.
• Overseeing the full ML lifecycle: From data ingestion and feature processing to annotation workflows, ensuring the platform facilitates frictionless, rapid model iteration for Data Sc.
• Own training infrastructure on Databricks and Unity Catalog: Make training fast, reproducible, and traceable. Lineage matters.
• Model serving: Build the serving layer — low-latency APIs, batch scoring jobs, appropriate caching. Integrate with our Java/Spring services.
• Monitoring and drift: Build the observability our models need — data drift, model drift, accuracy regression, business metrics. Grafana dashboards, Prometheus metrics, clear alerts.
• Driving AI tooling adoption: Model how AI-native development works for platform teams. Claude Code, agentic workflows, AI-assisted incident response.

Skills and requirements

• Relevant technical, electrical, mechanical, maintenance, gas, plumbing, or engineering experience may be required.

Candidate fit

• Setting examples in approach and behaviour is a key trait.
• Systems Architect: You think about the platform as a product with real users (your ML team). You design APIs, write docs, and measure adoption.
• Technical leader: You lead through standards, RFCs, and credibility — not meetings. You've mentored MLOps engineers into senior ICs.

Additional role context

• Report into our Infrastructure and Data Engineering organisation, working in close partnership with the AI/ML team in Prague.