DevOps Engineer | Luma

Role overview

Build and operate delivery infrastructure for cloud-hosted scientific software. This DevOps role combines AWS and Kubernetes operations with pipeline automation, monitoring, and security practices. It also calls for using AI-assisted tools thoughtfully and supporting the deployment and operation of AI and machine learning workloads.

Responsibilities

- Design and maintain CI/CD and deployment pipelines for AWS-based and Kubernetes applications, automating builds, tests, analysis, and releases. - Manage cloud infrastructure and help product teams plan deployments and apply sound cloud and container practices. - Monitor applications after release and improve observability, alerting, and incident investigation. - Integrate security checks into delivery pipelines and apply controls for access, networks, encryption, secrets, and container environments. - Support vulnerability response, incident handling, audit readiness, and compliance work. - Build and operate infrastructure for AI and machine learning workloads, including model serving, GPU-enabled environments, and cost monitoring; establish safeguards for AI-assisted engineering.

Requirements

- More than eight years of experience maintaining high-traffic applications or comparable infrastructure; the source listing specifies this experience threshold. - Strong practical knowledge of Kubernetes and AWS services, such as compute, storage, databases, load balancing, and networking. - Experience with infrastructure as code and CI/CD tools, and understanding of software delivery processes and version control. - Familiarity with monitoring and logging tools, cloud networking, and production operations. - Knowledge of DevSecOps practices, cloud identity and security controls, container and software supply-chain security, scanning, and secrets management. - Practical use of AI coding or operations assistants, plus an understanding of deploying and observing AI or machine learning workloads and related risks, including data exposure and insecure generated code.

Nice to have

- Scripting with Python or Bash, Linux administration, or experience with microservices and serverless systems. - GitOps tooling, internal DevOps automation or agents, or relevant cloud and Kubernetes certifications. - Experience with relational or document databases. - Openness to incorporating AI tools into day-to-day engineering work.