Platform Engineer

Overview

Our client, an early-stage company building advanced AI systems, is seeking a senior platform engineer to take ownership of their core platform. This is not a traditional Dev Ops position focused purely on CI/CD; the role spans GPU orchestration, multi-cloud Kubernetes environments, real-time networking, and observability. The company is already running production workloads across multiple clusters, regions, and hardware types, and is actively expanding into additional cloud providers.

This hire will play a key role in scaling and stabilizing that infrastructure.

• Design and manage multi-region Kubernetes clusters across cloud and GPU-focused providers using infrastructure-as-code
• Own the deployment lifecycle through Git Ops practices (Helm, Kustomize, automated releases, continuous delivery)
• Manage GPU infrastructure, including scheduling efficiency, workload placement, and 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 across identity, secrets, and encryption
• Maintain CI/CD workflows supporting a monorepo of services and deployment artifacts
• Partner closely with ML engineers to optimize model serving and GPU utilization

• Strong experience operating Kubernetes in production environments, including troubleshooting, autoscaling, and upgrades
• Proven background with infrastructure-as-code tools (e.g., Terraform, Pulumi)
• Hands-on experience running GPU workloads on Kubernetes and understanding resource optimization
• Familiarity with Git Ops tooling such as ArgoCD or Flux, and Helm-based deployments
• Experience with in-memory data systems (e.g., Redis) and distributed architectures
• Solid understanding of observability tooling and practices
• Strong networking fundamentals, particularly in low-latency or distributed systems
• Experience working in environments with broad ownership across infrastructure

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