Director, AI Platform Engineering & DevOps

IQVIA•Wayne, PA

3d•$119,900 - $334,200

About The Position

The Director, AI Platform Engineering & DevOps leads strategy, architecture, engineering, and operational enablement for enterprise AI, Kubernetes, GPU, and DevOps platforms. The role partners with infrastructure engineering, application development, data science teams, and business stakeholders to develop scalable, secure, cost-effective, and operationally sustainable AI and cloud-native platforms. It supports adoption of Private AI, Generative AI, GPU-based computing, Kubernetes platform services, RunAI capabilities, automation, and DevOps practices. The position helps business and technical teams evaluate AI use cases, onboard workloads, optimize infrastructure investments, and improve developer and data science productivity. It develops architecture recommendations, technical proposals, investment and cost-optimization plans, and future-state roadmaps for AI platform growth. The role also promotes platform adoption through discovery, workshops, demonstrations, onboarding, and enablement, while ensuring solutions align with enterprise standards, information security expectations, operational processes, and infrastructure best practices. The Director works under limited direction to lead complex initiatives across multiple teams, collaborates with Enterprise Architecture, and mentors technical resources to expand platform support and knowledge transfer. A bachelor’s degree in a related field is preferred; equivalent combinations of education, training, certifications, and relevant experience may be considered.

Requirements

Extensive experience in enterprise infrastructure architecture, platform engineering, Kubernetes, DevOps, cloud-native technologies, AI/ML infrastructure, or high-performance computing environments.
Experience designing and supporting enterprise Kubernetes or container platforms.
Experience supporting DevOps, CI/CD, automation, and infrastructure as code practices.
Experience architecting GPU-based infrastructure for AI, machine learning, or high-performance computing workloads.
Experience with AI/ML platforms, Generative AI, LLM hosting approaches, or Private AI solutions.
Experience partnering with developers, data scientists, architects, and business stakeholders to translate requirements into technical solutions.
Experience performing vendor evaluations, technical comparisons, platform recommendations, and cost-benefit analysis.
Experience leading complex cross-functional technology initiatives from concept through implementation and operational support.
Very strong written and verbal communication skills, including preparing proposals, summaries, roadmaps, and executive-ready recommendations.
Strong knowledge of Kubernetes, container orchestration, platform engineering, and cloud-native architecture.
Strong knowledge of DevOps practices, CI/CD pipelines, GitOps, infrastructure as code, automation, and operational process improvement.
Strong understanding of GPU infrastructure, AI/ML workloads, LLM infrastructure requirements, and high-performance computing design.
Ability to evaluate complex technical options and recommend scalable, cost-effective, enterprise-ready solutions.
Ability to translate complex AI, infrastructure, and platform concepts into clear business and leadership recommendations.
Strong stakeholder management skills and ability to influence across infrastructure, architecture, development, data science, and business teams.
Strong analytical and problem-solving skills focused on performance, scalability, resiliency, cost optimization, and operational readiness.
Ability to lead technical discovery sessions, workshops, demos, onboarding sessions, and enablement activities.
Ability to mentor technical resources and support knowledge transfer across teams.
Ability to work under limited direction and lead complex initiatives across multiple teams and priorities.
A bachelor’s degree in Computer Science, Information Technology, Engineering, Data Science, or a related field is preferred; equivalent education, training, certifications, and relevant enterprise technology experience may be considered.

Nice To Haves

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

Responsibilities

Define and drive architecture strategy for enterprise AI, Private AI, Generative AI, GPU, Kubernetes, and cloud-native platform services.
Lead the design and evolution of scalable GPU infrastructure for AI, machine learning, LLM, data science, and high-performance computing workloads.
Provide Kubernetes platform leadership in workload orchestration, containerized platform design, resource management, scalability, reliability, and operational governance.
Advance DevOps practices, 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.
Drive platform adoption through workshops, technical discovery sessions, onboarding, demos, and enablement sessions.
Support platform users through onboarding, troubleshooting, technical guidance, requirements analysis, and operational support.
Ensure AI infrastructure solutions are designed to meet 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 to align AI and platform engineering capabilities with broader enterprise technology direction.
Identify opportunities to improve developer productivity, enable on-premises AI alternatives, reduce public cloud AI service costs, and support business-driven AI initiatives.
Mentor and guide junior or supporting resources, improve knowledge transfer, and reduce dependency on senior architecture resources.
Ensure platform solutions align with enterprise standards, InfoSec expectations, operational processes, and infrastructure best practices.