Executive Director, AI Infrastructure & Platform Engineering

CVS Health•York, PA

1d•$175,100 - $334,750•Onsite

About The Position

The Executive Director, AI Infrastructure & Platform Engineering is a senior engineering leadership role responsible for standing up, operating, and continuously improving CVS Health's on-premises AI compute platform. This position owns the physical and platform layers of CVS’s Enterprise AI Factory — a frontier-class GPU compute environment running NVIDIA Blackwell systems across a high-throughput RoCE v2 fabric, hosted in co-located data center facilities, with multi-site expansion underway. Reporting to the Global Head of Infrastructure/AI Operations and Service Delivery, this leader will establish operational baselines across the full infrastructure stack — hardware, network fabric, GPU clusters, storage, and the operating systems and orchestration layers above — and build the Site Reliability Engineering practice that delivers the availability, reliability, and performance that frontier AI workloads demand. This is a greenfield organizational build. The Executive Director will define the operating model, set the engineering standards, hire and develop the team, and establish the long-term operations capability that will govern CVS's AI infrastructure for years ahead.

Requirements

10+ years of engineering leadership experience, with substantial time directly owning physical infrastructure at data center scale — including hardware lifecycle, capacity planning, and facility coordination (power, cooling, rack-and-stack execution).
Hands-on production ownership of bare-metal Kubernetes or OpenShift. Managed cloud services (EKS, GKE, AKS) alone do not substitute for the practitioner expertise this role requires.
Fluency with high-speed cluster fabrics — RoCE v2, InfiniBand, EVPN-VXLAN, or carrier-grade equivalent — and the operational discipline these fabrics require (PFC, ECN, lossless tuning, congestion management).
5+ years leading multiple technical teams simultaneously, including 24/7 operations organizations, with measurable team health, retention, and performance outcomes.
Proven success establishing and enforcing operational baselines, SLO / SLI / error-budget frameworks, and observability-driven continuous improvement in physical-infrastructure-anchored environments.
Hardware lifecycle, vendor accountability, and facility coordination experience — including capacity planning, RMA management, and multi-vendor escalation.
Experience leading operational transitions or organizational build-outs at scale, with business continuity and minimal disruption as non-negotiables.
Executive-level stakeholder communication, vendor negotiation, and budget ownership.
Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or related technical field.

Nice To Haves

Hands-on experience with Cisco UCS, NVIDIA HGX / DGX / Blackwell systems, and VAST or comparable distributed NVMe storage.
Direct experience operating GPU clusters of 32 or more GPUs in production environments — including HPC, AI training, research computing, or comparable workloads.
NVIDIA AI Enterprise, NVIDIA Run:AI, NVIDIA Base Command Manager, or comparable GPU orchestration platform experience.
Healthcare or other regulated-industry background (HIPAA, NIST AI RMF, SOX, FedRAMP, ITAR).
Chaos engineering and AI-driven operations experience — predictive alerting and automated remediation patterns.
Background in innovation programs, POD structures, or centers of excellence.

Responsibilities

Define and execute the long-range vision and strategy for AI infrastructure and platform engineering, with availability (>99.99%), reliability, and platform performance as the primary measures of success.
Recruit, hire, develop, and retain a high-performing engineering organization spanning infrastructure, network, platform reliability, observability, security, 24/7 operations, change and release management, and FinOps.
Establish clear ownership, accountability, and performance expectations across all functional teams; foster a culture of operational excellence, engineering rigor, and continuous improvement.
Provide executive-level communication to senior leadership on platform status, milestones, risk posture, and strategic initiatives.
Own the physical layer of the AI compute environment — GPU compute, storage, network fabric, capacity planning, and hardware lifecycle accountability.
Direct bare-metal Kubernetes and OpenShift operations, including cluster administration, GPU quota governance, infrastructure-as-code adoption, and availability baseline enforcement.
Govern high-performance network fabric operations — RoCE v2, spine-leaf topology, lossless Ethernet tuning, congestion management, and segmentation.
Establish and enforce operational baselines across every layer of the stack — hardware, fabric, platform, and workload — with deviations detected, escalated, and resolved within defined SLAs.
Direct Innovation POD strategy to develop self-healing and autonomous capabilities that proactively prevent service degradation before it impacts availability.
Build and sustain a high-performing 24/7 operations model — designed for sustainable, predictable coverage with no mandatory overtime and measurable team health and retention.
Drive end-to-end observability across the physical and platform layers, with continuous feedback loops connecting monitoring data to incident response, change decisions, and improvement cycles.
Oversee change management so every modification is risk-assessed, monitored during rollout, and baseline-validated post-deployment.
Ensure configuration consistency and drift detection across all platform components to prevent baseline degradation over time.
Lead GPU FinOps governance — utilization optimization, tenant quota enforcement, and cost reduction — in partnership with the Finance organization.
Empower the Security SRE Lead to maintain a world-class security posture across the infrastructure and platform layers, with robust compliance to frameworks including HIPAA and NIST AI RMF.
Govern access controls, audit logging, vulnerability management, and network segmentation across the AI compute environment.
Lead the operational transition from program-launch staffing to permanent CVS-owned operations — governing phased handoffs, competency validation, and milestone sign-offs to ensure minimal disruption to platform availability and business operations.
Establish and lead the long-term operating model by institutionalizing key technical, architectural, and delivery leadership capabilities into permanent CVS roles, ensuring the organization is fully self-sustaining at program close.
Own vendor relationships, contract performance, and accountability across the hardware, networking, platform, and managed-services stack.
Manage budget ownership for the AI infrastructure and platform engineering organization, including capital planning and operational expense governance.