Director, AI Platform Engineering & DevOps

IQVIA•Wayne, PA

2d•$119,900 - $334,200

About The Position

The Director, AI Platform Engineering & DevOps will be responsible for leading the strategy, architecture, engineering, and operational enablement of enterprise AI, Kubernetes, GPU, and DevOps platforms. This role partners with infrastructure engineering, application development, data science teams, and business stakeholders to design scalable, secure, cost-effective, and operationally sustainable AI and cloud-native platforms. The role supports enterprise adoption of Private AI, Generative AI, GPU-based computing, Kubernetes-based platform services, RunAI capabilities, automation, and DevOps practices. The position is accountable for helping business and technical teams evaluate AI use cases, onboard workloads, optimize infrastructure investments, and accelerate developer and data science productivity.

Requirements

Requires extensive experience in enterprise infrastructure architecture, platform engineering, Kubernetes, DevOps, cloud-native technologies, AI/ML infrastructure, or high-performance computing environments.
Designing and supporting enterprise Kubernetes or container platforms.
Supporting DevOps, CI/CD, automation, and infrastructure as code practices.
Architecting GPU-based infrastructure for AI, machine learning, or high-performance compute workloads.
Working with AI/ML platforms, Generative AI, LLM hosting approaches, or Private AI solutions.
Partnering with developers, data scientists, architects, and business stakeholders to translate requirements into technical solutions.
Performing vendor evaluations, technical comparisons, platform recommendations, and cost-benefit analysis.
Leading complex cross-functional technology initiatives from concept through implementation and operational support.

Nice To Haves

Experience with RunAI or similar AI/GPU orchestration platforms preferred.
Experience with NVIDIA GPU platforms and AI infrastructure ecosystems preferred.
Experience with hybrid cloud, private cloud, virtualization, networking, storage, and enterprise compute platforms preferred.
Experience supporting AI adoption, developer enablement, workshops, platform onboarding, or technical evangelization preferred.
Experience preparing executive-level architecture recommendations, investment proposals, and technical roadmaps preferred.

Responsibilities

Define and drive architecture strategy for enterprise AI, Private AI, Generative AI, GPU, Kubernetes, and cloud-native platform services.
Lead design and evolution of scalable GPU infrastructure to support AI, machine learning, LLM, data science, and high-performance compute workloads.
Provide Kubernetes platform leadership, including workload orchestration, containerized platform design, resource management, scalability, reliability, and operational governance.
Advance DevOps practices across platform services, including CI/CD enablement, automation, infrastructure as code, configuration management, deployment repeatability, and operational efficiency.
Partner with developers, data scientists, application architects, business architects, enterprise architecture, and business leaders to assess AI use cases and determine appropriate platform solutions.
Evaluate technology options, vendor capabilities, infrastructure designs, GPU configurations, networking, storage, and platform tooling to support enterprise AI objectives.
Drive adoption of AI platform services by conducting workshops, technical discovery sessions, onboarding activities, demos, and enablement sessions for development and data science teams.
Support platform users through onboarding, troubleshooting, technical guidance, requirements analysis, and operational support.
Work with business and technical teams to ensure AI infrastructure solutions are not treated as simple checklist items, but are designed correctly for application, availability, migration, and business requirements.
Develop technical proposals, business cases, architecture recommendations, and cost optimization plans for AI and GPU platform investments.
Lead capacity planning and future-state roadmap development for AI platform growth, GPU expansion, workload onboarding, and Private AI adoption.
Collaborate with Enterprise Architecture teams to align AI and platform engineering capabilities with broader enterprise technology direction.
Identify opportunities to improve developer productivity, enable on-prem AI alternatives, reduce public cloud AI service costs, and support business-driven AI initiatives.
Mentor and guide junior or supporting resources to scale platform support, improve knowledge transfer, and reduce dependency on senior architecture resources.
Ensure platform solutions are implemented in alignment with Enterprise Standards, InfoSec expectations, operational processes, and infrastructure best practices.