DevOps Engineer | Luma

Dotmatics; United States; Remote; USD 120,000 - 170,000; Full time

Job summary

Dotmatics is seeking a DevOps Engineer to optimize and secure our cloud-native, Kubernetes-based scientific software platform. You will design pipelines, automate deployments, and work with product teams to implement best practices across AWS, CI/CD, and security.

This role emphasizes AI-assisted tooling and robust monitoring in a remote-friendly environment. You will collaborate across global teams to harden infrastructure, apply policy-as-code, and ensure compliance with industry standards

Qualifications

• Experience designing and operating CI/CD pipelines for AWS-native and Kubernetes-based applications.
• Strong understanding of cloud security, IAM, and least-privilege controls.
• Experience with AI-assisted tooling to improve pipeline development and incident analysis.
• Familiarity with policy-as-code and CIS/NIST benchmarks.

Responsibilities

• Design and manage CI/CD and deployment pipelines for AWS-native and Kubernetes-based applications.
• Advise product teams on deployment requirements and best practices.
• Collaborate on cloud/container security and cost controls.
• Manage cloud infrastructure in production environments.
• Automate builds, tests, and analysis with CI tools.
• Monitor performance post-release and improve monitoring systems.
• Lead DevOps strategy for AI/ML workloads and guardrails.

Skills

Kubernetes

AWS

IaC

CI/CD

Monitoring

Networking

DevSecOps

Scripting

GitOps

AI for DevOps

Tools

Octopus Deploy

GitHub Actions

Jenkins

TeamCity

CodePipeline

Datadog

Grafana

Prometheus

Loki

ELK

App Insights

Job description

Описание

Our Why At Dotmatics At Dotmatics, a Siemens company, we believe science, data, and decision-making must be deeply intertwined for innovation to thrive. Our Portfolio includes Luma, LumaLab Connect, ELN Platform, Graphpad Prism, Geneious, SnapGene, Protein Metrics, OMIQ, FCS Express, LabArchives, NQuery, EasyPanel, SoftGenetics and Virscidian. We have a vision for a new Lab of the Future that will change the future of scientific research. We have created the world’s most comprehensive digital science platform – best-of-breed software applications already used by more than 2 million scientists, together in a single ecosystem united by a powerful, flexible enterprise data platform. This is not flat data buried away in digital graveyards. This is dynamic, multi-dimensional decision-making. Scientific enterprises need a new level of effectiveness to achieve tomorrow’s breakthroughs. Illness will not wait. The biosphere will not wait. We are tireless in our vision, because the time for innovation is now. Shaping the Future of Science At Dotmatics Our global team of more than 800 colleagues are dedicated to supporting our customers in over 180 countries. Together, with our scientific community of users, we accelerate scientific innovation in order to make the world a healthier, cleaner, and safer place to live. You’ll join a collaborative, global team pushing the boundaries of scientific innovation. Your ideas and efforts will have a tangible impact, accelerating scientific progress and discovery. We offer a dynamic, remote-friendly environment that fosters high integrity and collaboration, empowering you to excel. Dotmatics is a company built by scientists, for scientists. Combined, we are now the world’s largest cloud-based scientific research R&D platform. We need your help to keep growing and pioneering the future. We are Science Driven. We are Customer Centric. We are Better Together. 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

Задачи

• 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

Требования

• 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

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