2 days 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 and cloud and container best practices.
- Manage cloud infrastructure, DevOps processes, infrastructure-as-code, and deployment automation.
- Automate code changes, builds, testing, analysis, and release workflows using CI tools.
- Monitor released applications and improve automated monitoring, alerting, and performance analysis.
- Embed DevSecOps practices including SAST, DAST, dependency, secrets, and container image scanning.
- Implement least-privilege IAM, network segmentation, encryption, secrets management, and Kubernetes security controls.
- Harden Kubernetes clusters and cloud environments using policy-as-code, admission controls, runtime protection, and CIS/NIST benchmarks.
- Support vulnerability management, incident response, audit readiness, and SOC 2 and ISO 27001 compliance efforts.
- Use AI-assisted tools for pipeline development, infrastructure code, troubleshooting, and incident analysis.
- Apply AI/ML-driven monitoring for 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 data 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 and customer cloud environments.
- Experience with AWS services, infrastructure-as-code, CI/CD tools, observability platforms, cloud networking, and version control.
- Knowledge of 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, or model serving on Kubernetes.
- Awareness of AI-specific risks including prompt injection, data leakage, and insecure AI-generated code.
- Scripting experience with Python or Bash is beneficial.
- Linux administration, networking, HTTP/2, microservices, serverless architectures, GitOps, internal tooling, AI-driven DevOps workflows, and ChatOps experience may be beneficial.
- Relevant certifications such as AWS Certified DevOps Engineer, AWS Security Specialty, CKA, or CKS may be beneficial.
- Experience with PostgreSQL, MySQL, or MongoDB may be beneficial.
Benefits
- Remote-friendly work environment with a collaborative global team.
- Opportunity to work on scientific software products and infrastructure used by a global scientific community.
- 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.