DevOps Engineer | Luma

What do we need

Dotmatics is seeking a DevOps Engineer to join our team working on ground-breaking Scientific Software products such as Luma.  In this role, you will have the opportunity to apply your knowledge to help improve our processes, infrastructure and security by applying best practices with the latest technologies. You will be AI fluent and know how AI influences engineering and DevOps 

In this role you will get to:

• Design and manage CI/CD and deployment pipelines for both AWS-native and Kubernetes-based applications
• Provide advice/assistance to the product teams, clarifying requirements for deployment
• Collaborate with product teams on cloud and container best practices
• Manage IT infrastructure in cloud computing environments
• Automation: Use CI tools to automate code changes, builds, testing, and analysis
• Monitoring: Monitor performance after release
• Develop and improve monitoring systems for automated alerts
• Strategy: Design and manage the DevOps infrastructure, pipelines, and processes
• SECURITY: Embed security into every stage of the delivery lifecycle (DevSecOps), including automated SAST, DAST, dependency, secrets, and container image scanning in CI/CD pipelines
• SECURITY: Implement and enforce least-privilege IAM, network segmentation, encryption in transit and at rest, and secrets management (e.g. AWS Secrets Manager, Vault, KMS)
• SECURITY: Harden Kubernetes clusters and cloud environments using policy-as-code, admission controls, runtime protection, and CIS/NIST benchmarks
• SECURITY: Support vulnerability management, incident response, audit readiness, and compliance efforts (e.g. SOC 2, ISO 27001)
• AI: Use AI-assisted tooling to accelerate pipeline development, infrastructure code, troubleshooting, and incident analysis
• AI: Apply AI/ML-driven capabilities to monitoring, including anomaly detection, alert noise reduction, and automated root-cause analysis
• AI: Build and operate deployment pipelines and infrastructure for AI/ML and LLM-based workloads, including model serving, GPU-enabled clusters, and cost controls
• AI: Establish guardrails for responsible AI use in engineering workflows, including data privacy, access controls, and review of AI-generated code and configuration

We are looking for people who have 8+ years of experience maintaining high-traffic applications and complex technical infrastructure. You will be someone with a proven background in supporting cloud environments, with hands-on experience translating AWS Well-Architected principles into action.

The key skills we are looking for:

• Kubernetes clusters/customer cloud environments
• AWS (ECS, EKS, ALB, EC2, S3, RDS, CloudFront, Route53, etc.)
• Infrastructure-as-code (Terraform, CloudFormation, Pulumi)
• CI/CD tools (Octopus Deploy, GitHub Actions, Jenkins, TeamCity, CodePipeline, etc.)
• APMs and logging (e.g. Datadog, Grafana, Prometheus, Loki, ELK, App Insights)
• Networking concepts in cloud environments
• Software development lifecycle processes/version control
• Security: DevSecOps practices, AWS security services (IAM, GuardDuty, Security Hub, WAF, Config, KMS), container and supply chain security (image signing, SBOMs), and security scanning tools (e.g. Trivy, Snyk, SonarQube, Checkov)
• Security: Familiarity with Kubernetes security (RBAC, network policies, Pod Security Standards, OPA/Kyverno) and secrets management
• AI: Practical experience using AI coding and operations assistants (e.g. GitHub Copilot, Claude, Amazon Q) to improve engineering productivity
• AI: Understanding of how to deploy and operate AI/ML workloads (e.g. Amazon Bedrock, SageMaker, model-serving on Kubernetes) and observe their performance and cost
• AI: Awareness of AI-specific risks such as prompt injection, data leakage, and insecure AI-generated code

You may also have:

• Scripting languages (e.g. Python, Bash)
• Linux administration and networking (e.g. HTTP/2)
• Microservice and serverless architectures
• Experience with GitOps tooling (e.g. Argo CD, Flux)
• Experience building internal tooling or agents that apply AI to DevOps workflows (e.g. automated incident triage, ChatOps)
• Relevant certifications (e.g. AWS Certified DevOps Engineer, AWS Security Specialty, CKA/CKS)
• Databases (e.g. PostgreSQL, MySQL, MongoDB)
• Exposure to AI-enabled tooling or workflows, with openness to incorporating AI into day-to-day work