DevOps / Platform Engineer - Cloud/AI Infrastructure
New
BrazilFull-TimeMiddle
Salary not disclosed
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Job Details
- Languages
- English
- Experience
- 6+ years
- Required Skills
- AWSPythonBashGCPJenkinsKubernetesMLFlowTypeScriptAzureGoGrafanaPrometheusTerraformGitHub ActionsAzure DevOpsDatadogCloudFormationMLOps
Requirements
- 6+ years of experience in DevOps, SRE, or Platform Engineering roles, including hands-on work with production systems.
- Strong experience with Infrastructure as Code tools such as Terraform, Pulumi, or CloudFormation.
- Proven experience managing Kubernetes environments (EKS, AKS, or GKE) including deployment, scaling, and troubleshooting.
- Deep knowledge of CI/CD pipelines using tools such as GitHub Actions, GitLab CI, Jenkins, or Azure DevOps.
- Hands-on experience with at least two major cloud providers (AWS, Azure, GCP).
- Strong scripting and automation skills (Python, Bash, or similar), plus familiarity with Go or TypeScript.
- Experience building observability stacks using tools like Prometheus, Grafana, Datadog, ELK, or OpenTelemetry.
- Strong understanding of cloud security, IAM, networking, and compliance best practices.
- Experience with GitOps workflows (ArgoCD, Flux, or equivalent).
- Exposure to MLOps concepts such as model serving, MLflow, or GPU-based infrastructure is highly valued.
- Strong communication skills, fluency in English, and experience working in international teams.
- Ability to mentor engineers and contribute as a technical leader is a strong plus.
Responsibilities
- Design, build, and maintain end-to-end CI/CD pipelines for applications, infrastructure, data workflows, and AI/ML models with automated testing and security gates.
- Develop and operate cloud-agnostic infrastructure across AWS, Azure, and GCP using Infrastructure as Code tools such as Terraform or equivalent.
- Manage Kubernetes-based environments for scalable workloads, including AI model serving and containerized applications.
- Build and support MLOps infrastructure including model training environments, deployment pipelines, registries, and monitoring systems.
- Implement robust observability solutions (metrics, logs, traces) and ensure full system visibility across services and AI workloads.
- Design secure infrastructure with strong IAM, secrets management, network policies, and compliance with industry regulations (SOC 2, ISO 27001, GDPR).
- Improve developer experience by building internal platforms, self-service tools, and automation workflows.
- Support reliability engineering through SLOs, incident management, disaster recovery planning, and chaos engineering practices.
- Collaborate with engineering and product teams to ensure scalable, efficient, and secure delivery pipelines.
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