Senior AI Platform Engineering Lead

EY; Columbus (OH); Remote; USD 144,000 - 359,000; Full time

Benefits offered by this job

Medical and dental coverage

401(k) plan

Paid time off

Flexible vacation policy

Job summary

EY seeks a senior leader to head the AI Platform Infrastructure Engineering group, directing a team of AI Infrastructure and AI Systems Engineers. You will own the delivery roadmap, shape cloud-native platform layers, and personally design and troubleshoot critical infrastructure components across cloud, on-prem, edge, and air-gapped environments.

The role demands hands-on leadership in Kubernetes, GPU, and multi-tenant scaling, with a focus on reliability, portability, and safe model execution.

Qualifications

• Bachelor’s or Master’s degree in Computer Science or a related technical field.
• 10+ years building or operating enterprise infrastructure, cloud platforms, or large-scale Kubernetes environments.
• 3+ years leading or directing engineering teams while remaining hands-on.
• Experience owning platform/infrastructure roadmaps end-to-end.

Responsibilities

• Lead the AI Platform Infrastructure Engineering team and set priorities.
• Personally design and debug the most critical platform components.
• Own the infrastructure delivery roadmap and entry/exit gates.
• Oversee secure execution, inference, and routing with AI frameworks and gateways.
• Define SLOs, incident management, and backup/disaster recovery practices.

Skills

Kubernetes

GPU infrastructure

Cloud platforms

AI inference serving

Incident leadership

DevOps & GitOps

Education

Bachelor’s or Master’s degree in Computer Science or related field

Tools

Kubernetes distributions (RKE2, EKS/AKS/GKE)

Terraform/OpenTofu

Helm

ArgoCD

Crossplane

Job description

EY seeks a senior leader to head the AI Platform Infrastructure Engineering group, directing a team of AI Infrastructure and AI Systems Engineers. You will own the delivery roadmap, shape cloud-native platform layers, and personally design and troubleshoot critical infrastructure components across cloud, on-prem, edge, and air-gapped environments.

The role demands hands-on leadership in Kubernetes, GPU, and multi-tenant scaling, with a focus on reliability, portability, and safe model execution.