Senior AI Platform Engineer — Cloud-Native Infra

EY; Austin (TX); Hybrid; USD 126,000 - 230,000; Full time

Benefits offered by this job

Hybrid work model

Medical and dental coverage

Pension and 401(k)

Paid time off

Flexible vacation policy

Job summary

EY is seeking AI Systems Engineers to build and operate the foundational substrate for EY's AI-native platform. You will own the cloud-native platform, Kubernetes, and multi-tenant infrastructure across cloud, on‑prem, edge, and air-gapped targets, enabling reliable model execution and scalable workloads.

This role requires deep hands-on expertise with bare-metal, GPU systems, and production Kubernetes at scale, with a focus on reliability, portability, and compliance in regulated client

Qualifications

• Bachelor's or Master's degree in Computer Science or related field.
• 8+ years building or operating enterprise infrastructure or Kubernetes environments.
• Hands-on with Kubernetes distributions and full cluster lifecycle management.
• Experience across cloud, on‑prem, edge, and air-gapped deployment models.

Responsibilities

• Own the cluster & cloud-native platform including Kubernetes, scheduling, and multi-tenancy.
• Manage infrastructure foundation: Ubuntu, bare-metal, DPU, and NVIDIA environments.
• Stand up EY Agentic AI environments across cloud and on‑prem with consistent stack.
• Deliver infrastructure management, Kubernetes, scheduling, and cluster fabric capabilities.
• Oversee backup, DR, and cross‑environment replication for high availability.

Skills

Kubernetes

Cloud-native infra

Multi-tenancy

GPU infrastructure

Infra-as-code

GitOps

Security compliance

V2 Ray Serve

Education

Bachelor's or Master's in Computer Science

Tools

RKE2

EKS/AKS/GKE

K3s

Terraform/OpenTofu

Helm

ArgoCD

Crossplane

NVIDIA DCGM

Job description

EY is seeking AI Systems Engineers to build and operate the foundational substrate for EY's AI-native platform. You will own the cloud-native platform, Kubernetes, and multi-tenant infrastructure across cloud, on‑prem, edge, and air-gapped targets, enabling reliable model execution and scalable workloads.

This role requires deep hands-on expertise with bare-metal, GPU systems, and production Kubernetes at scale, with a focus on reliability, portability, and compliance in regulated client