Staff DevOps Engineer

About This Opportunity

We are representing a confidential SaaS organization seeking a Staff DevOps Engineer to lead the design and evolution of their platform engineering practice, with deep technical ownership of Kubernetes infrastructure.

This individual-contributor technical leadership role will help define how engineering teams build, deploy, and operate software by establishing platform architecture, standards, and paved roads that make infrastructure easier to consume without sacrificing reliability, security, or scalability.

Our client operates a globally available, redundant microservices architecture and positions reliability, API accessibility, and adaptability as key elements of its technology platform. This role will help evolve the infrastructure behind that environment while supporting the growing needs of engineering and AI/ML workloads.

Location: Mexico. Candidates located within 80 kilometers of Guadalajara may be expected to work onsite to support collaboration.

Responsibilities

• Own the architecture and roadmap for multi-cluster Kubernetes platform, including scaling, upgrades, and multi-tenancy
• Establish GitOps-based deployment workflows using technologies such as Argo CD or Flux
• Design and evolve an internal developer platform that provides engineering teams with self-service access to compute, environments, and observability
• Architect service mesh, networking, and ingress strategies for reliable and secure communication across services
• Define platform standards for GPU and ML workload scheduling and resource management on Kubernetes
• Partner with AI/ML teams on infrastructure requirements for training and inference workloads
• Drive capacity planning and cost optimization across Kubernetes and cloud infrastructure
• Set technical direction and review architecture for platform-impacting changes across engineering
• Define and own platform reliability through SLOs, incident-response leadership, and postmortems
• Mentor experienced engineers and represent platform engineering in cross-organizational technical decisions
• Influence teams toward common platform standards without relying on direct reporting authority

What We're Looking For

• Bachelor's degree in Computer Science or related field, or equivalent work experience
• 8+ years of DevOps, platform, or infrastructure engineering experience
• 5+ years of hands-on Kubernetes experience in production, including cluster architecture, upgrades, and multi-tenant environments
• Strong hands-on experience operating cloud infrastructure across AWS and Google Cloud Platform (GCP)
• Strong GitOps experience with Argo CD or Flux
• Strong Infrastructure-as-Code experience with Terraform or Pulumi
• Deep understanding of container orchestration, networking, ingress, and service mesh technologies such as Istio, Linkerd, or Cilium
• Experience with middleware and messaging technologies such as Nginx, Kafka, and Redis at scale
• Experience with GPU scheduling, node pools, and resource quotas supporting ML/AI training or inference workloads in GKE and EKS
• Experience designing internal developer platforms or paved-road tooling for engineering organizations
• Strong Linux, networking, storage, and security fundamentals
• Experience operating observability platforms such as Prometheus, Grafana, Datadog, or OpenTelemetry at platform scale
• Excellent communication skills with the ability to influence technical direction across teams
• Demonstrated ability to understand, calculate, forecast, and optimize cloud and Kubernetes infrastructure costs
• Experience with capacity planning and resource optimization, balancing performance, reliability, scalability, and infrastructure cost

Preferred qualifications:

• Kubernetes policy-as-code and security tooling such as OPA/Gatekeeper, Kyverno, or image-scanning technologies
• AWS, GCP, Azure, CKA, or CKS certifications
• Experience designing infrastructure capable of supporting ML/AI training or inference workloads
• Understanding of GPU scheduling, resource allocation, node pools, quotas, reliability, and cost considerations for AI workloads

How We Work With You

When you apply through Get A Job.ai, our talent team will review your profile and screen qualified candidates. We'll then present your background to our client for consideration. If selected, you'll interview directly with the hiring organization through our coordinated process.

Important: Please do not attempt to contact the client directly. All communication and interview coordination will be managed through Get A Job.ai to ensure a professional and organized process for all parties.

Pay

Compensation details will be discussed during the screening process and will be competitive with the market and commensurate with your experience and qualifications.

Equal Employment Opportunity: Get A Job.ai is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.