AI Platform Infra Engineer – Kubernetes & Hybrid Cloud

EY; Baton Rouge (LA); On-site; USD 126,000 - 230,000; Full time

Benefits offered by this job

Hybrid work model

Competitive compensation

Total Rewards package

Job summary

EY seeks an AI Systems Engineer to build and operate EY’s AI-native platform, spanning bare-metal to Kubernetes, with multi-tenant scaling and PCI-compliant, portable substrate for Agentic AI workflows.

You will manage cloud and on‑prem environments (EKS/AKS/GKE, RKE2, K3s) and drive reliability, security, and portability across diverse targets, including edge and air-gapped deployments.

Qualifications

• Bachelor’s or Master’s degree in Computer Science or related technical field.
• 8+ years building or operating enterprise infrastructure, cloud platforms, or large-scale Kubernetes environments.
• Hands-on expertise with Kubernetes distributions (RKE2, EKS/AKS/GKE, K3s) and full cluster lifecycle management.
• Deep experience with bare-metal, cloud, hybrid, on-prem, and ideally air-gapped deployment models.
• Strong grounding in cluster networking (Cilium/service mesh/CNI), storage, and multi-tenancy isolation.
• Experience with GPU infrastructure and scheduling (NVAIE/DCGM, GPU operators, virtualization/MIG).
• Experience with infrastructure-as-code and GitOps tooling (Terraform/OpenTofu, Helm, ArgoCD, Crossplane).
• Proven track record operating production infrastructure under compliance, security, or regulatory constraints.
• Ability to work effectively with security, architecture, product, and delivery teams.

Responsibilities

• Own the cluster & cloud-native platform: compute, Kubernetes and scheduling, cluster fabric/networking, multi-tenancy, and distributed compute, as the substrate for Agentic AI workflows and tooling.
• Own the infrastructure foundation: Ubuntu/OS, BMC/bare-metal, DPU architecture, and NVAIE (GPU/Network/DCGM), ensuring the physical and virtual bedrock is provisioned, patched, and production-ready.
• Stand up and manage EY Agentic AI environments across cloud (EKS/AKS/GKE), on-prem AI Factory (RKE2/NVAIE), edge (K3s), and air-gapped deployment modes, maintaining one consistent stack contract across all targets.
• Deliver foundational platform capabilities such as Infrastructure Management, Kubernetes & Scheduling, and Cluster Fabric Management, so downstream runtime, data, and execution services can run safely and consistently.
• Own cluster lifecycle, autoscaling, GPU pooling/virtualization, and multi-tenancy boundaries (vCluster/Crossplane/Karpenter), providing isolated, elastic capacity per tenant and engagement.
• Own secure execution and inference: Ray Serve, vLLM/NIM/Triton, and NVIDIA Dynamo, with sandboxed execution (gVisor/Firecracker for hosted, NVIDIA OpenShell/vNode for on-prem) for isolated, safe model execution.
• Own cognitive and routing: Envoy AI Gateway, semantic routing (vLLM-SR), model/prompt selection, and streaming response handling — directing each request to the right model under the right constraints.
• Collaborate with DevOps Engineers on deployment and delivery of the platform itself: CI/CD/CV (ArgoCD), infrastructure-as-code / GitOps (Helm/OpenTofu), so environments are reproducible and drift-free.
• Own backup, disaster recovery, and cross-environment replication for high availability (Velero, CloudNativePG, Cilium ClusterMesh), along with patching and platform supply-chain hygiene.
• Ensure the substrate is modular and swappable, so components can be replaced without rewriting consumers, minimizing vendor lock-in while preserving the stack contract.

Skills

Kubernetes

Cloud infrastructure

Bare-metal GPU

Multi-tenancy

CI/CD

Education

Bachelor's or Master's in Computer Science

8+ years enterprise infrastructure

Tools

RKE2

Terraform

ArgoCD

Crossplane

Job description

EY seeks an AI Systems Engineer to build and operate EY’s AI-native platform, spanning bare-metal to Kubernetes, with multi-tenant scaling and PCI-compliant, portable substrate for Agentic AI workflows.

You will manage cloud and on‑prem environments (EKS/AKS/GKE, RKE2, K3s) and drive reliability, security, and portability across diverse targets, including edge and air-gapped deployments.