> Markdown version of [/jobs/ext/3894817-ai-systems-engineer-ai-platforms-manager](https://www.wearedevelopers.com/jobs/ext/3894817-ai-systems-engineer-ai-platforms-manager). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Systems Engineer - AI Platforms - Manager - **Company:** Ernst & Young LLP - **Location:** Oklahoma City, OK, United States - **Experience:** Expert - **Salary:** $125,500.0 - $230,200.0 - **Contract:** Permanent contract - **Skills:** OpenTofu, Artificial Intelligence, Systems Engineering, Ubuntu (Operating System), Cloud Computing, Cloud Foundry, Continuous Integration, DevOps, Disaster Recovery, Virtualization Technology, System Availability, Agentic-AI, AI Platforms, Kubernetes, Information Technology, Bare Metal, Ray Serve, Slurm, Hardware Infrastructure, Nim (Programming Language), Terraform - **Published:** October 23, 2026 - **Apply:** https://www.oklahomacityjobsite.com/job.asp?id=3466632027&tx=JR2317FFK&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54 ## About the Role * Deep expertise in cloud-native platform engineering (Kubernetes, networking, multi-tenancy at scale) and low-level infrastructure and systems engineering (bare-metal, GPU, DPU). * Advanced understanding of how compute, networking, storage, and scheduling interact to form a reliable, portable substrate. * Comfortable operating across cloud, on-prem, edge, and air-gapped environments simultaneously, with a portability-first mindset. * Strong command of AI gateways, semantic routing, and model/prompt selection under latency and cost constraints. * A passion for ensuring reliability, repeatability, and reduction of operational toil through automation and infrastructure-as-code. * Ability to define and honor clean ownership boundaries with adjacent trust, data, and runtime teams. * Strong communicator able to explain infrastructure tradeoffs to engineers, architects, and leadership. * Natural product-ownership orientation toward foundational platforms, measured by the downstream velocity and reliability they enable. To qualify you must have * 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, including hands-on systems depth. * 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. Ideally, you'll also have * Experience supporting AI/ML and GPU-intensive workloads (GPU virtualization, MIG, AI workload scheduling). * Experience with multi-tenant platforms and tenancy isolation at scale (e.g., vCluster). * Experience with disaster recovery, backup, and cross-environment replication tiered by RPO/RTO. * Familiarity with distributed compute and batch orchestration (Kueue, Slurm/Slinky, Ray, OpenMPI). * Background in platform or SRE roles where success is measured by downstream team velocity, reliability, and portability. * Exposure to regulated delivery environments (financial services, tax, healthcare, risk). ## Description We are seeking AI Systems Engineers to build and operate the foundational substrate that powers EY's AI-native platform. This role owns the infrastructure and cloud-native platform layers of the Hybrid AI Multi-Environment Runtime (HAI), from bare-metal and GPU infrastructure through Kubernetes, cluster fabric, and multi-tenant scaling. You will be responsible for building and managing EY Fabric environments across cloud, on-prem, edge, and air-gapped targets., * 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.