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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Platform Engineering Lead (Senior Manager) - **Company:** Ernst & Young LLP - **Location:** Columbus, OH, United States (Remote available) - **Experience:** Expert - **Salary:** $172,800.0 - **Contract:** Permanent contract - **Skills:** OpenTofu, Artificial Intelligence, Amazon Web Services, Systems Engineering, Microsoft Azure, Ubuntu (Operating System), Cloud Computing, Cloud Foundry, Code Review, Continuous Integration, Software Debugging, DevOps, Disaster Recovery, Open Source Technology, Virtualization Technology, AI Infrastructure, System Availability, Agentic-AI, AI Platforms, Kubernetes, Information Technology, Bare Metal, Ray Serve, Slurm, Hardware Infrastructure, Nim (Programming Language), VLLM, Model Inference, Terraform - **Published:** October 15, 2026 - **Apply:** https://dejobs.org/x/x/C93273BE60C24AA98B4238C40CE98E88/job/ ## About the Role 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., * 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 Kubernetes distributions (RKE2, EKS/AKS/GKE, K3s) and full cluster lifecycle management at production scale. * 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. * Hands-on experience with GPU infrastructure and scheduling (NVAIE/DCGM, GPU operators, virtualization/MIG) and AI inference serving (e.g., vLLM, NIM, Triton, Ray Serve). * Experience with infrastructure-as-code and GitOps tooling (Terraform/OpenTofu, Helm, ArgoCD, Crossplane). * Experience with disaster recovery, backup, and cross-environment replication tiered by RPO/RTO. * Proven track record operating production infrastructure under compliance, security, or regulatory constraints, including incident leadership and SLO ownership. * Experience managing infrastructure budgets, capacity plans, and vendor relationships. * Ability to work effectively with security, architecture, product, and delivery teams, and to represent and defend technical decisions to senior stakeholders. Ideally, you'll also have * Experience leading the build of an AI/ML or GPU-intensive platform (GPU virtualization, MIG, AI workload scheduling) from early stage to production. * Experience with multi-tenant platforms and tenancy isolation at scale (e.g., vCluster). * Familiarity with distributed compute and batch orchestration (Kueue, Slurm/Slinky, Ray, OpenMPI). * Experience with sandboxed and confidential execution technologies (gVisor, Firecracker, confidential computing). * Background in platform or SRE leadership roles where success is measured by downstream team velocity, reliability, and portability. * Exposure to regulated delivery environments (financial services, tax, healthcare, risk). * Relevant certifications (e.g., CKA/CKS, NVIDIA AI Infrastructure, AWS/Azure/GCP professional-level). ## Description 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., * 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.