Director, AI Platform Engineering & DevOps

About the role

The Director, AI Platform Engineering & DevOps leads the strategy, architecture, engineering and operational enablement of enterprise AI, Kubernetes, GPU and DevOps platforms. The role partners with infrastructure, application development, data science teams and business stakeholders to build scalable, secure and cost-effective AI and cloud-native platforms, supporting Private AI, Generative AI, GPU computing, Kubernetes services, RunAI capabilities, automation and DevOps practices.

Key responsibilities

• Define and drive the architecture strategy for enterprise AI, Private AI, Generative AI, GPU, Kubernetes and cloud-native platform services.
• Lead design and evolution of scalable GPU infrastructure for AI, ML, LLM, data science and high-performance compute workloads.
• Provide Kubernetes platform leadership, including orchestration, containerized design, resource management, scalability, reliability and operational governance.
• Advance DevOps practices: CI/CD, automation, infrastructure as code, configuration management, deployment repeatability and operational efficiency.
• Partner with developers, data scientists, architects and business leaders to assess AI use cases and choose platform solutions.
• Evaluate technology options, vendors, GPU configurations, networking, storage and platform tooling.
• Drive adoption through workshops, discovery sessions, onboarding, demos and enablement, and support users with troubleshooting and guidance.
• Develop technical proposals, business cases, architecture recommendations and cost optimization plans for AI and GPU investments.
• Lead capacity planning and the roadmap for AI platform growth, GPU expansion and Private AI adoption.
• Mentor junior resources, ensure alignment with enterprise standards and InfoSec, and identify ways to improve developer productivity and reduce public cloud AI costs.

Required profile

• Extensive experience in enterprise infrastructure architecture, platform engineering, Kubernetes, DevOps, cloud-native technologies, AI/ML infrastructure or high-performance computing.
• Bachelor's degree in Computer Science, Information Technology, Engineering, Data Science or a related field preferred; equivalent education, training, certifications and experience may be considered.
• Ability to work under limited direction and lead complex initiatives across multiple teams.

Required skills

• Strong knowledge of Kubernetes, container orchestration, platform engineering and cloud-native architecture.
• DevOps practices, CI/CD pipelines, GitOps, infrastructure as code and automation.
• GPU infrastructure, AI/ML workloads, LLM infrastructure and high-performance compute design.
• Vendor evaluation, technical comparisons, cost-benefit analysis and executive-level architecture recommendations.
• Preferred: RunAI or similar AI/GPU orchestration platforms, NVIDIA GPU platforms, hybrid cloud, private cloud, virtualization, networking, storage and enterprise compute platforms, and experience enabling AI adoption and developer onboarding.