1 day ago
Remote, United KingdomSenior
Responsibilities
- Design and manage CI/CD and deployment pipelines for AWS-native and Kubernetes-based applications.
- Advise product teams on deployment requirements, cloud practices, container practices, and infrastructure design.
- Manage cloud IT infrastructure and automate code changes, builds, testing, and analysis.
- Monitor released applications, improve monitoring systems, and develop automated alerting.
- Design and manage DevOps infrastructure, pipelines, and operating processes.
- Embed DevSecOps practices into delivery, including automated security, dependency, secrets, and container image scanning.
- Implement IAM, network segmentation, encryption, secrets management, Kubernetes hardening, policy controls, and runtime protection.
- Support vulnerability management, incident response, audit readiness, and compliance efforts.
- Use AI-assisted tools for pipeline development, infrastructure code, troubleshooting, and incident analysis.
- Apply AI/ML-driven monitoring capabilities such as anomaly detection, alert-noise reduction, and automated root-cause analysis.
- Build and operate infrastructure and deployment pipelines for AI/ML and LLM workloads, including model serving and GPU-enabled clusters.
- Establish responsible-AI guardrails covering privacy, access controls, and review of AI-generated code and configuration.
Requirements
- At least 8 years of experience maintaining high-traffic applications and complex technical infrastructure.
- Proven experience supporting cloud environments and applying AWS Well-Architected principles.
- Hands-on experience with Kubernetes clusters, customer cloud environments, AWS services, infrastructure as code, CI/CD tools, observability platforms, and cloud networking.
- Experience with DevSecOps, AWS security services, container and software supply-chain security, security scanning, Kubernetes security, and secrets management.
- Practical experience using AI coding and operations assistants such as GitHub Copilot, Claude, or Amazon Q.
- Understanding of deploying and operating AI/ML workloads using technologies such as Amazon Bedrock, SageMaker, and model serving on Kubernetes.
- Awareness of AI-specific risks including prompt injection, data leakage, and insecure AI-generated code.
- Preferred experience with Python or Bash, Linux administration, networking, GitOps tooling, internal DevOps tooling, AI-powered incident workflows, and databases.
- Relevant certifications such as AWS Certified DevOps Engineer, AWS Security Specialty, CKA, or CKS are beneficial.
- Experience with microservice and serverless architectures and openness to incorporating AI into day-to-day work are beneficial.
Benefits
- Remote-friendly work environment with a collaborative global team.
- Opportunity to work on cloud-based scientific research software used by scientists worldwide.
- Equal opportunity workplace focused on inclusion, support, and connection.
Tech Stack
Argo CDAWSBashDatadogGitHub ActionsGrafanaJenkinsKubernetesLinuxMongoDBMySQLOctopus DeployPostgreSQLPrometheusPythonSonarQubeTeamCityTerraform
Categories
About Dotmatics
Dotmatics builds cloud-based R&D scientific software that unifies biology, chemistry, and data management workflows for biopharma, chemicals/materials companies, and academic labs; products include Luma, ELN, GraphPad Prism, Geneious, SnapGene, Protein Metrics, and LabArchives. The company sells SaaS and enterprise licenses for data integration, analysis, and decision support, is headquartered in Boston, and operates as a Siemens company following its acquisition.