DevOps Engineer II

We are seeking an experienced Senior DevOps Engineer to join our 20+ member global engineering team supporting Autonomics, our AI-powered operations platform. The ideal candidate will be responsible for maintaining, optimizing, and supporting our existing infrastructure solutions while ensuring reliable operations and implementing improvements across our development and operations teams. This role will report to our existing DevOps SRE and work collaboratively with global teams including SRE, infrastructure, release engineering, InfoSec, Product Support, and QA. The successful candidate will be a problem-solver who thrives in a fast-paced, global environment and has strong communication skills across time zones, supporting high-throughput applications with large datasets.ResponsibilitiesDesign, deploy, and manage Kubernetes clusters in production environments supporting high-volume AI applications handling up to 100 req/sAdminister Linux systems (RedHat family) with focus on performance optimization and securityImplement and maintain container orchestration using Docker and Helm chartsConfigure and optimize load balancing solutions for high-availability applicationsLead technical initiatives and architect solutions for complex infrastructure challengesAdminister and optimize Percona MySQL databases handling 100GB - 1TB datasets per application instanceMonitor and optimize existing Redis implementations for performance and reliabilitySupport and maintain OpenSearch clusters that handle analytics for our Autonomics platform, processing denormalized operational and historical dataManage index lifecycle, performance tuning, data retention policies, and cluster scaling strategies for OpenSearchImplement comprehensive monitoring, logging, and alerting systemsDesign and maintain backup strategies and disaster recovery proceduresMonitor and optimize disk performance and storage solutionsImplement security best practices across infrastructure components and manage SSL/TLS certificatesOptimize network performance including HTTP/2 implementationDevelop automation scripts using Bash, Golang, Python, Terraform for infrastructure managementSupport Java application deployment and optimization for our Autonomics platformCreate and maintain CI/CD pipelines (Bamboo, Gitlab)Collaborate with development teams on application architecture and deployment strategiesImplement GitOps workflows and Infrastructure as Code practicesQualifications/Attributes
Required Qualifications:7+ years of experience in DevOps or Infrastructure Engineering roles with demonstrated ability to lead technical initiativesExpert-level knowledge of Kubernetes and container orchestration in production environmentsStrong Linux system administration skills (RedHat family)Proficiency with Docker containerization and Helm package managementPercona MySQL administration and optimization for large-scale datasets (100GB-1TB range)Redis administration, performance optimization, and troubleshooting of existing implementationsOpenSearch cluster management with experience in index management, performance tuning, data retention policies, cluster scaling, and analytics workloadsExperience with monitoring and logging solutions at scaleKnowledge of backup and disaster recovery strategiesUnderstanding of load balancing concepts and implementationFamiliarity with HTTP/2 protocol and web performance optimizationExperience with disk performance tuning and storage optimizationStrong experience with GitOps workflows and Infrastructure as Code tools (Terraform or similar)Proficiency in Bash and Python for automation and toolingUnderstanding of Java applications and deployment requirementsStrong communication skills across global time zones and ability to work in hybrid environment
Preferred Qualifications:Go programming language knowledgeCloud platform experience (OCI, AWS, GCP, Azure)Experience with observability tools like Prometheus, Grafana, or ELK stackHands-on experience with WAF configuration and managementExperience with chaos engineering or reliability testingKnowledge of AI/ML application deployment patterns
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