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Site Reliability Engineer- AI & ML Infrastructure (Kubernetes, AWS & Terraform) | WithMira

Role overview

Site Reliability Engineer- AI & ML Infrastructure (Kubernetes, AWS & Terraform)

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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.

Are passionate about building platforms that empower developers and researchers.
Enjoy creating elegant, automated solutions for complex infrastructure challenges in both cloud and data center environments.
Thrive on optimizing hybrid infrastructure for performance, cost, and reliability.
Are excited to work at the intersection of modern platform engineering and cutting-edge AI.
Love to treat infrastructure as a product, continuously improving the developer experience.

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).

Experience with CI/CD systems (e.g., GitLab CI, Jenkins, ArgoCD) and building developer tooling.
Familiarity with FinOps principles and cloud cost optimization strategies.
Knowledge of Kubernetes networking (e.g., Calico, Cilium) and storage (e.g., Ceph, Rook) solutions.
Experience in a multi-region or hybrid cloud environment.