Director, AI Platform Engineering & DevOps

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.

Essential Functions

• 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.

Experience Required

Requires extensive experience in enterprise infrastructure architecture platform engineering Kubernetes DevOps cloud-native technologies AI/ML infrastructure or high-performance computing environments.

Experience should include several of the following:

• 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.

Education Required

• Bachelors Degree in Computer Science Information Technology Engineering Data Science or a related field preferred.
• Equivalent combination of education training certifications and relevant enterprise technology experience may be considered.

Additional Experience

• 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.

Key Skills and Abilities

• Very strong written and verbal communication skills including the ability to prepare 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 compute 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 with the ability to influence across infrastructure architecture development data science and business teams.
• Strong analytical and problem-solving skills with a focus 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.

IQVIA is a leading global provider of clinical research services commercial insights and healthcare intelligence to the life sciences and healthcare industries. We create intelligent connections to accelerate the development and commercialization of innovative medical treatments to help improve patient outcomes and population health worldwide. Learn more at

IQVIA is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race color religion sex sexual orientation gender identity national origin disability status as a protected veteran or any other status protected by applicable law. is committed to integrity in our hiring process and maintains a zero tolerance policy for candidate fraud. All information and credentials submitted in your application must be truthful and complete. Any false statements misrepresentations or material omissions during the recruitment process will result in immediate disqualification of your application or termination of employment if discovered later in accordance with applicable law. We appreciate your honesty and professionalism.