Lead AWS-Kubernetes Platform Engineer (Remote)

Improvado

Buenos Aires

Remote

ARS 184,482,000 - 230,602,000

Full time

3 days ago

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Benefits offered by this job

Professional growth

Competitive USD-based pay

Exciting projects

Flextime

Job summary

AgileEngine is seeking a Senior/Lead DevOps Engineer to deploy and operationalize a Kubernetes-based cloud environment on AWS for a short-term project. You will lead deployments and ensure security, reliability, and scalability while collaborating with application, infra, and security teams.

The role requires 5+ years in DevOps/SRE, strong Kubernetes experience, IaC tooling, and fluent English. This is a 100% remote position with competitive USD-based compensation and opportunities to influence

Qualifications

5+ years in DevOps/SRE/Platform Eng/Cloud Eng.
4+ years supporting Kubernetes in production.
Experience deploying AWS-hosted apps.
Experience leading technical impl. with minimal supervision.
Strong troubleshooting and RCA skills.
Kubernetes, EKS, and Helm.
Argo CD and Service Mesh technologies.
Ingress controllers, networking, storage, upgrades.
AWS: VPC, EC2, EKS, IAM, Route 53, ALB/NLB, RDS, CloudWatch, S3, Secrets Manager, Systems Manager.
GitLab, CI/CD, GitOps, IaC, Terraform.
Bash, Python, or Go.
Upper-intermediate English level.

Responsibilities

Lead deployment of a new cloud environment on AWS or other provider.
Ensure deployment follows security, reliability, and operational best practices.
Collaborate with application, infra, security, and operations teams.
Deploy and manage workloads on Kubernetes platforms.
Troubleshoot cluster, networking, ingress, storage, and workload issues.
Support modernization and containerization efforts.
Implement platform standards and guardrails.
Design and enhance GitLab CI/CD pipelines.
Implement deployment automation and release workflows.
Establish GitOps deployment patterns with Argo CD.
Improve traceability, repeatability, and deployment reliability.
Develop infrastructure with Terraform and related tooling.
Reduce manual deployment activities through automation.
Configure platform monitoring, logging, alerting, and dashboards.
Support production readiness reviews and runbooks.
Establish reliability practices and incident response.
Identify bottlenecks across cloud and Kubernetes environments.
Document platform architecture and procedures.
Produce runbooks and knowledge-transfer materials.