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
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