Senior MLOps & Infrastructure Engineer (DevOps/MLOps)

What the Team Covers

You will contribute across all of the following over time. We do not expect one person to master every part on day one, but we do expect you to be willing to work in any of them.

• GPU and HPC clusters running model training, large-scale inference and annotation workloads
  

• MLOps: model serving, pipeline orchestration, experiment and model registry
  

• Storage at multi-petabyte scale: tiering, lifecycle, backup and recovery
  

• Ingestion of large volumes of recorded sensor data, with integrity verification and metadata extraction
  

• CI/CD, GitOps, infrastructure-as-code, monitoring and observability
  

• Access control, audit logging and data protection for internal and external users
  

Key Responsibilities
AI Compute & Serving

• Operate and scale GPU clusters across training, inference and annotation workloads; manage scheduling, utilisation and capacity
  

• Deploy and scale high-throughput model serving; tune GPU memory and runtime performance
  

• Manage multi-GPU distributed training and reproducible environment sandboxing
  

• Scale pipeline orchestration on Kubernetes for large data processing jobs
  

Platform Automation & Delivery

• Drive GitOps-based deployment and maintain infrastructure-as-code across the platform
  

• Build and maintain CI pipelines, secure container builds and release automation
  

• Build monitoring, logging and alerting so that failures are detected and actionable, never silent
  

• Lead incident response and drive follow-up actions to closure
  

Data Infrastructure & Security

• Operate storage at multi-petabyte scale: object storage, local storage, tiering, backup and recovery
  

• Operate the ingestion path for large sensor data deliveries, with integrity verification and metadata extraction
  

• Implement access control and single sign-on across platform services, with audit logging
  

• Apply data protection measures to sensitive content before it reaches external users