DevOps Engineer

The role keeps a cloud imaging platform running across several environments, with Kubernetes, infrastructure as code and GitOps as the daily tools. A strong candidate has a few years of hands on platform or reliability work, is comfortable on Linux and AWS, and can write clear documentation and change records. Care with security, patching and cost control matters as much as speed of delivery.

How it fits the AI era

AI work here means supporting the science team's training and model deployment environments alongside the production product, so the platform has to serve both. Much of the routine build, deploy and monitoring work can be automated, which frees time for judgement on reliability, security and regulated change control. The human part is owning decisions, incident response and keeping systems auditable.

How it builds AI experience

There is a chance to learn machine learning infrastructure first hand, including experiment tracking and model packaging, in a company whose product is AI driven imaging analysis. Building familiarity with MLflow style tooling, model registries and secure model deployment would strengthen a case for this role. Experience with regulated release cycles or medical imaging data would add further weight.

Worth showing

• Terraform or OpenTofu
• GitLab CI/CD pipelines
• AWS cost management
• Clear technical documentation

This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.

About Qureight

Qureight provides AI-powered imaging and precision endpoints for lung and heart clinical trials, per the company.

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