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The opportunity

We are seeking a senior leader for our AI Platform Engineering group, the team that builds and operates the foundational substrate powering EY's AI-native platform. This role is accountable for the infrastructure and cloud-native platform layers of our AI runtime, from bare-metal and GPU infrastructure through Kubernetes, cluster fabric, secure inference, and multi-tenant scaling. You will set the technical direction for a team of AI Infrastructure and AI Systems Engineers, and be accountable for standing up and running EY AI environments across cloud, on-prem, edge, client-managed, and air-gapped targets.

This is a player-coach role. You will direct the team's work and own the delivery roadmap, while remaining a hands-on individual contributor who personally designs, builds, and troubleshoots the most critical and complex parts of the platform. The role is ideal for a senior engineering leader who is as comfortable troubleshooting a cluster configuration as they are in leading an executive review, who can set a phased roadmap and hold delivery gates under pressure, 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.

Your key responsibilities

+ Lead and develop the AI Platform Infrastructure Engineering team: direct the day-to-day and longer-term work of AI Infrastructure and AI Systems Engineers, set priorities, allocate work, and remove blockers so the team delivers predictably; hire, mentor, and coach engineers, set engineering standards (design reviews, code reviews, runbooks), and grow technical depth and leadership capability across the team.

+ Build hands-on as a senior individual contributor: personally design, implement, and debug the hardest and highest-risk components of the platform - cluster bring-up, GPU and networking enablement, multi-tenancy, inference and routing - and lead by example on engineering quality.

+ Own the infrastructure delivery roadmap: sequence the build into phases that each put a usable capability into engineering teams' hands, define and defend entry and exit gates, and report progress, risk, and trade-offs to senior leadership.

+ Own the infrastructure foundation and cloud-native platform: from Ubuntu/OS, BMC/bare-metal, DPU architecture, and NVAIE (GPU/Network/DCGM) through compute, Kubernetes and scheduling, cluster fabric and networking, multi-tenancy, and distributed compute - delivering foundational capabilities (Infrastructure Management, Kubernetes & Scheduling, Cluster Fabric Management) as a provisioned, patched, production-ready substrate for Agentic AI workflows and downstream runtime, data, and execution services.

+ Stand up and operate EY Agentic AI environments: across cloud (EKS/AKS/GKE), on-prem AI Factory (RKE2/NVAIE), edge (K3s), client-managed, and air-gapped deployment modes, maintaining one consistent stack contract across all targets and keeping the substrate modular and swappable so components can be replaced without rewriting consumers or creating vendor lock-in.

+ Own secure execution, inference, and routing: Ray Serve, vLLM/NIM/Triton, and NVIDIA Dynamo, with sandboxed execution (NVIDIA OpenShell) for isolated, safe model execution; and Envoy AI Gateway, semantic routing (vLLM-SR), model and prompt selection, and streaming response handling - directing each request to the right model under the right constraints.

+ Own operational excellence, resilience, and platform delivery: define SLOs, on-call and incident management practices, and lead post-incident reviews; own backup, disaster recovery, and cross-environment replication for high availability (Velero, CloudNativePG, Cilium ClusterMesh), patching, security hardening, and supply-chain hygiene; and partner with DevOps Engineers on CI/CD/CV (ArgoCD) and infrastructure-as-code / GitOps (Helm/OpenTofu) so environments are reproducible and drift-free.

+ Own GPU capacity, platform economics, and vendor selection: forecast demand, set quotas and cost attribution, and manage the infrastructure budget; evaluate commercial against open-source options, run proofs of concept, manage strategic vendor relationships, and work with procurement on long-lead hardware and licensing, making the commercial case for capacity to senior leadership.

Skills and attributes for success

+ Deep, hands-on expertise in cloud-native platform engineering (Kubernetes, networking, multi-tenancy at scale) and low-level infrastructure and systems engineering (bare-metal, GPU, DPU) - able to go deeper than anyone on the team when required.

+ Proven ability to lead as a player-coach: balancing personal technical contribution with directing, unblocking, and developing a team of senior engineers.

+ Judgment about sequencing and trade-offs: able to identify the minimum that makes a platform genuinely usable, ship it, and defend what can safely wait.

+ Advanced understanding of how compute, networking, storage, and scheduling interact to form a reliable, portable substrate, and the architectural maturity to set standards others build on.

+ Comfortable operating across cloud, on-prem, edge, client-managed, and air-gapped environments simultaneously, with a portability-first mindset.

+ Strong command of AI inference serving, AI gateways, semantic routing, and model and prompt selection under latency and cost constraints.

+ A passion for reliability, repeatability, and the reduction of operational toil through automation and infrastructure-as-code, and the ability to instil that culture in a team.

+ Executive presence and strong communication skills, able to explain infrastructure trade-offs, delivery risk, budget, and resourcing needs to engineers, architects, partners, and senior leadership.

+ Calm, decisive leadership under pressure, including during production incidents and critical delivery milestones.

+ 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 a related technical field.

+ 10+ years building or operating enterprise infrastructure, cloud platforms, or large-scale Kubernetes environments, including deep, current hands-on systems depth.

+ 3+ years leading or directing engineering teams (technical leadership of multiple engineers), while remaining an active hands-on contributor.

+ Track record of owning a platform or infrastructure roadmap and architecture end to end, from requirements and estimation through phased delivery and steady-state operations.

+ Hands-on expertise with Kuberne...

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