Site Reliability Engineer – AI & ML Infrastructure, Kubernetes, AWS, Terraform

100% remote Flexible hours Hiring now

Job Description:

Architect and maintain our core computing platform using Kubernetes on AWS and on-premise, providing a stable, scalable environment for all applications and services.
Develop and manage our entire infrastructure using Infrastructure-as-Code (IaC) principles with Terraform, ensuring our environments are reproducible, versioned, and automated.
Design, build, and optimize our AI/ML job scheduling and orchestration systems, integrating Slurm with our Kubernetes clusters to efficiently manage GPU resources.
Provision, manage, and maintain our on-premise bare metal server infrastructure for high-performance GPU computing.
Implement and manage the platform's networking (CNI, service mesh) and storage (CSI, S3) solutions to support high-throughput, low-latency workloads across hybrid environments.
Develop a comprehensive observability stack (monitoring, logging, tracing) to ensure platform health, and create automation for operational tasks, incident response, and performance tuning.
Collaborate with AI researchers and ML engineers to understand their infrastructure needs and build the tools and workflows that accelerate their development cycle.
Automate the life cycle of single-tenant, managed deployments

Requirements:

5+ years of experience in Platform Engineering, DevOps, or Site Reliability Engineering (SRE)
Proven, hands-on experience building and managing production infrastructure with Terraform
Expert-level knowledge of Kubernetes architecture and operations in a large-scale environment
Experience with high-performance compute (HPC) job schedulers, specifically Slurm, for managing GPU-intensive AI workloads
Experience managing bare metal infrastructure, including server provisioning (e.g., PXE boot, MAAS), configuration, and lifecycle management
Strong scripting and automation skills (e.g., Python, Go, Bash)

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