AI Infrastructure Director

Overview

About Kirkland & Ellis. At Kirkland & Ellis, we don’t just meet the standard for legal excellence—we set it. Our culture is built on teamwork, ingenuity and an unwavering commitment to continuous growth. We tackle the most sophisticated legal challenges with bold ideas and innovative solutions, powered by the exceptional experience and ambition of our 7,000+ people, including 4,000+ attorneys, across 23 offices worldwide.

Our dedicated professionals share our lawyers’ commitment to excellence and show up each day to do meaningful work that helps drive global business, investment and innovation forward.

Are you energized by building and scaling enterprise-grade AI platforms that power innovation across a complex organization?

As AI Infrastructure Director
, you’ll design, manage, and optimize the firm’s AI infrastructure—spanning on‑premise GPU environments and Microsoft Azure–based AI platforms—to enable enterprise AI, automation, and innovation initiatives at scale.

This role sits at the intersection of technology, governance, and business impact. You’ll be the single point of accountability for AI-specific environments and shared AI platform services, partnering closely with Cloud Engineering, AI Engineering, and the Chief Growth Office (CGO) to ensure secure, reliable, and scalable delivery aligned with firm priorities. You’ll also lead and grow a multi‑disciplinary engineering organization responsible for the operational excellence of the firm’s AI platforms.

• AI Infrastructure Ownership: Lead all AI environments—including on‑premise Graphics Processing Unit (GPU) clusters, Microsoft Azure AI and Machine Learning (ML) services, and shared AI platform components—with accountability for reliability, scalability, and lifecycle management.
• Azure AI Environment Leadership: Own Azure environments hosting AI and automation workloads, including shared services such as Azure OpenAI, Azure AI Foundry, Azure AI Search, and Azure Kubernetes Service (AKS).
• Cross‑Team Partnership: Collaborate with Cloud Engineering on landing zones, networking, subscription governance, and service onboarding, and with the AI Engineering Lead on shared platforms and operating standards.
• Innovation Enablement: Create secure, governed environments that enable rapid experimentation and development for Innovation, AI Engineering, and CGO teams.
• Team Leadership & Development: Lead AI Infrastructure, AI Platform Engineering, Azure AI Engineering Operations, and Microsoft 365 (M365) Automation functions; mentor leaders and engineers and build sustainable career paths.
• Platform Design & Delivery: Oversee the design and deployment of shared and custom AI platforms that accelerate solution delivery while meeting security and governance standards.
• Security & Responsible AI: Operationalize governance, privacy, and Responsible AI standards in partnership with Risk, Security, and Responsible AI teams.
• Operational Excellence: Ensure platform reliability, service‑level objectives, incident response readiness, and continuous improvement across production AI environments.
• Strategic Planning & Vendor Management: Manage cloud operating budgets, vendor relationships, and capacity planning across Azure services, GPU infrastructure, and AI tooling.

• Education &
  
  Certifications:
  
  Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field required;
  Master’s degree or Master of Business Administration (MBA) strongly preferred. Advanced Microsoft Azure certifications (e.g., Azure Solutions Architect Expert, Dev Ops Engineer Expert) strongly preferred.
• Leadership
  
  Experience:
  
  12+ years in infrastructure, platform, or cloud engineering within complex enterprises, including at least 5 years in senior leadership roles managing managers and multi‑team organizations.
• AI Platform Expertise: 5+ years designing, operating, and scaling production AI and ML platforms, including MLOps, Continuous Integration and Continuous Delivery (CI/CD), Infrastructure‑as‑Code, and containerized platforms such as Kubernetes and Docker.
• Azure at Scale: Deep expertise with enterprise‑scale Microsoft Azure, including AI…