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
