Location - Mexico, Colombia or Argentina (remote) Role Overview We're looking for an experienced DevOps / Cloud Engineer to join our global DevOps team. You'll be part of a core group responsible for the reliability, scalability, and automation of our multi-cloud SaaS platform running in AWS and Azure. The environment supports hundreds of enterprise tenants and requires exceptional operational maturity, automation, and observability. Key Responsibilities • Design, implement, and maintain CI/CD pipelines using GitOps principles (GitHub Actions, ArgoCD, etc.) • Drive automation across infrastructure provisioning, deployments, and operational workflows • Own and improve observability and monitoring, leveraging Elastic Stack or equivalent (Prometheus, Grafana, Datadog) • Develop tools and scripts (Python, Go, Bash, PowerShell) to enhance developer experience and efficiency • Manage and optimize Kubernetes infrastructure using Helm, Argo, and KEDA • Implement and maintain infrastructure-as-code across multi-cloud environments (AWS, Azure) • Participate in on-call rotations ensuring platform reliability and fast incident response • Perform root-cause analysis and postmortems for infrastructure or software failures • Collaborate closely with engineering teams to embed operational excellence and reliability into the product Qualifications • Minimum 5 years of experience as a DevOps, SRE, or Cloud Engineer in a production SaaS environment • Strong hands-on experience with Kubernetes, Helm, and Argo • Proficient in YAML, Bash, Python, and Go • Deep understanding of CI/CD pipelines, Git, and GitOps workflows • Experience with monitoring and observability tools such as Elastic Stack, Prometheus, or Datadog • Solid understanding of AWS and/or Azure cloud services • Excellent problem-solving skills, especially in debugging distributed systems • Strong communication and collaboration skills with a DevOps mindset Nice to Have • Experience with Terraform or Pulumi • Exposure to service mesh and secrets management solutions • Familiarity with cost optimization and performance tuning across cloud environments Originally posted on Himalayas