DevOps Engineer | Luma

Role overview

Build and operate delivery systems and cloud infrastructure for scientific software, supporting both AWS-based and Kubernetes applications. The work combines reliable deployment automation and observability with security throughout the software lifecycle. It also includes applying AI-assisted engineering tools and supporting infrastructure for machine-learning workloads, with attention to privacy, operational performance, and cost.

Responsibilities

- Design and maintain CI/CD pipelines for builds, testing, analysis, and deployments across cloud-native and Kubernetes environments. - Advise product teams on deployment needs, cloud architecture, and container practices. - Manage and improve cloud infrastructure, DevOps processes, monitoring, and automated alerting. - Integrate security checks into delivery pipelines, including code, dependency, secrets, and container scanning. - Apply secure configuration practices for identity, networks, encryption, secrets, and Kubernetes, and support vulnerability response and audit readiness. - Use AI-assisted tools to improve development and incident analysis; support deployment and operation of AI or machine-learning workloads and establish safeguards for their use.

Requirements

- Eight or more years maintaining high-traffic applications, as indicated in the available listing. - Practical experience with Kubernetes and AWS services such as EKS, ECS, EC2, S3, RDS, load balancing, or content delivery. - Experience with infrastructure as code and CI/CD tooling, such as Terraform, CloudFormation, Pulumi, GitHub Actions, Jenkins, or comparable tools. - Familiarity with cloud networking, application monitoring and logging, and software development lifecycle practices. - Working knowledge of DevSecOps, cloud security controls, container security, vulnerability scanning, Kubernetes access and network policies, and secrets management. - Practical experience with AI coding or operations assistants, plus an understanding of deploying and observing AI/ML workloads and related risks such as data leakage or insecure generated code.

Nice to have

- Python or Bash, Linux administration, microservices or serverless systems, and GitOps tools such as Argo CD or Flux. - Experience building internal automation or AI-supported operations tooling, relevant cloud or Kubernetes certifications, or familiarity with relational and NoSQL databases. - Exposure to AI-enabled workflows and interest in applying them to day-to-day engineering.

Benefits and work setup

- Remote-friendly work in a globally distributed team. The listing describes a collaborative environment but does not specify particular compensation or benefit details.