AI Systems Engineer - AI Platforms - Manager

EY•Hartford, DC

3d•$125,500 - $261,600•Hybrid

About The Position

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. This is the substrate on which EY Agentic AI capabilities run. This role is ideal for a full-stack infrastructure leader who is equally comfortable with bare-metal and GPU systems and production Kubernetes at scale, who treats reliability and portability as non-negotiable in regulated client contexts, and who understands that the substrate is a product in its own right, measured by the velocity, safety, and portability it unlocks for every team building above it.

Requirements

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.
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.

Nice To Haves

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).

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.