Platform Engineer

Role overview

You will be a Senior Platform Engineer responsible for owning the company’s core platform for production AI systems. This role goes beyond traditional DevOps: it covers GPU orchestration, multi-cloud Kubernetes, real-time networking, and observability.

Responsibilities

• Design & manage multi-region Kubernetes clusters across cloud and GPU-focused providers using infrastructure-as-code
• Own the deployment lifecycle using GitOps practices (e.g., Helm, Kustomize, automated releases, continuous delivery)
• Manage GPU infrastructure, including:
  • scheduling efficiency
  • workload placement
  • cold-start optimization
• Oversee networking systems such as ingress, gateways, load balancing, and cross-region connectivity
• Build and maintain observability across metrics, logs, traces, and performance profiling
• Ensure infrastructure security around identity, secrets, and encryption
• Support CI/CD workflows for a monorepo of services and deployment artifacts
• Partner with ML engineers to optimize model serving and GPU utilization

Requirements

• Strong experience operating Kubernetes in production (troubleshooting, autoscaling, upgrades)
• Proven experience with infrastructure-as-code (e.g., Terraform, Pulumi)
• Hands-on experience running GPU workloads on Kubernetes and optimizing resources
• Familiarity with GitOps tooling such as Argo CD or Flux, and Helm-based deployments
• Experience with Redis or other in-memory/distributed systems and distributed architectures
• Strong understanding of observability tooling and practices
• Solid networking fundamentals for low-latency/distributed systems
• Ability to work with broad ownership across infrastructure

Preferred background

• Exposure to GPU cloud providers beyond major hyperscalers
• Experience with real-time/streaming infrastructure
• Proficiency in Go or Python
• Familiarity with ML model deployment and optimization
• Experience managing infrastructure costs, especially for GPU-heavy workloads