DevOps Engineer | Luma

Dotmatics is seeking a DevOps Engineer to support scientific software products such as Luma. The role focuses on improving deployment processes, cloud infrastructure, monitoring, and security, while applying AI tools and supporting AI/ML workloads.

Responsibilities:

• 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

Requirements:

• 8+ years of experience maintaining high-traffic applications and complex technical infrastructure
• proven background in supporting cloud environments, with hands-on experience translating AWS Well-Architected principles into action
• 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
• 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