* Salário: R$ 3.000 a R$ 6.000 por mês (estimado)

* O valor exibido é uma estimativa calculada com base em dados públicos e referências do mercado. Não garantimos que este seja o salário oferecido para esta vaga específica.

Área: Tecnologia da Informação

Nível: Junior

We are operating Kubernetes and Linux GPU infrastructure that emphasizes Volcano-based scheduling, reliability, and automation for AI compute at scale. As a Middle DevOps Engineer, you will administer Kubernetes and Linux environments, build Python and Bash automation for job workflows, and work closely with client stakeholders in a delivery team. Apply to help optimize compute performance and researcher experience for demanding AI workloads.

Responsibilities

Operate and tune GPU-enabled Kubernetes clusters and standalone Linux compute environments to keep scheduling and performance optimized
Manage Volcano job scheduling, including queue setup, POD execution, GPU allocation, and namespace quota enforcement
Administer Kubernetes end to end with namespaces, RBAC, resource quotas, and workload isolation approaches
Create and support Python and Shell automation to simplify job submission, resource provisioning, and system reporting
Work with orchestration, optimization, and observability teams to raise scheduling efficiency, improve capacity utilization, and streamline researcher workflows
Measure infrastructure health and resource utilization, supplying data and feedback for optimization and reporting needs
Enhance infrastructure, tooling, and automation workflows to improve performance, scalability, and usability
Ensure operations deliver a smooth and efficient experience for researchers running diverse AI and computational workloads

Requirements

Hands-on background with 2+ years of experience in DevOps or infrastructure engineering within complex, large-scale environments
Expertise in Kubernetes administration and orchestration, including namespaces, POD scheduling/distribution, PVC, NFS, and resource quota management
Practical experience with the Volcano scheduler for GPU job execution, queue configuration, and workload prioritization integrated with Kubernetes
Proven ability to operate GPU cluster environments in Kubernetes as well as on standalone Linux compute nodes
Advanced Python scripting skills for infrastructure automation, plus proficiency in UNIX Shell scripting such as Bash
Strong Linux system administration skills, including troubleshooting, performance tuning, and configuration management
Solid understanding of infrastructure automation and orchestration concepts and related tooling
Fluent English communication skills (spoken and written) for direct client interaction

Nice to have

Knowledge of Helm package management for Kubernetes applications
Familiarity with monitoring and observability solutions, particularly Prometheus, Grafana, and Loki
Skills in Infrastructure as Code tools such as Terraform
Background in multi-cloud Kubernetes environments including Amazon EKS and Google GKE
Understanding of Azure Networking including VPN, ExpressRoute, and network security
Familiarity with AI-assisted coding tools such as GitHub Copilot, ChatGPT, and Claude
Experience with hybrid (cloud and on-premises) scheduling and resource optimization