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About This Role
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.
What You’ll Do
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.
What You’ll Bring
- 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, DevOps 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 and ML services, networking, identity, security, governance, and cost management.
- Hybrid Infrastructure Knowledge: Experience integrating and operating on‑premise GPU and high‑performance computing environments with cloud platforms.
- Operational Accountability: Proven ownership of platform reliability, incident command, and service‑level objectives in regulated, compliance‑sensitive environments.
- Governance \& Risk Awareness: Working knowledge of Responsible AI, AI risk management, regulatory frameworks, and compliance standards such as SOC 2 and ISO 27001\.
- Financial \& Vendor Acumen: Experience managing cloud spend using Financial Operations (FinOps) practices and overseeing enterprise vendor and contract relationships.
- Executive Communication: Strong executive presence with the ability to advise senior leaders and influence cross‑functional stakeholders.
- Industry Context: Experience in legal, professional services, or similarly regulated environments preferred, including familiarity with legal technology ecosystems.
If you’re excited to shape enterprise AI platforms, lead high‑impact engineering teams, and enable responsible innovation at scale in this AI Infrastructure Director role, we’d love to hear from you.
Compensation
The base salary range below represents the low and high end of the salary range for this position in Chicago. This range may differ based on your geographic location and cost of living considerations. At Kirkland \& Ellis, we consider compensation more than just a base salary. We offer an exceptional range of flexible benefits including comprehensive healthcare, paid time off, and retirement. We also offer personal support and tailored learning and development opportunities all designed to help you realize your full potential both in life and at work.
Compensation Range:
Chicago: $302,000 \- $335,000
How to Apply
Thank you for your interest in Kirkland \& Ellis LLP. To complete an application and submit your resume, please click "Apply Now."
*Don't meet every job requirement? That's okay! If you're excited about this role but your experience doesn't perfectly fit every qualification, we encourage you to apply anyway. You may be just the right person for this role or others at Kirkland.*
Equal Employment Opportunity
All employment decisions, including the recruiting, hiring, placement, training availability, promotion, compensation, evaluation, disciplinary actions, and termination of employment (if necessary) are made without regard to the employee’s race, color, creed, religion, sex, pregnancy or childbirth, personal appearance, family responsibilities, sexual orientation or preference, gender identity, political affiliation, source of income, place of residence, national or ethnic origin, ancestry, age, marital status, military veteran status, unfavorable discharge from military service, physical or mental disability, or on any other basis prohibited by applicable law. \#LI\-Hybrid \#LI\-LC1
Salary Context
This $302K-$335K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $181K across 1996 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Kirkland & Ellis, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $178,940 based on 11,900 positions with disclosed compensation. Director-level AI roles across all categories have a median of $243,000. This role's midpoint ($318K) sits 78% above the category median. Disclosed range: $302K to $335K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Kirkland & Ellis AI Hiring
Kirkland & Ellis has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Chicago, IL, US, Houston, TX, US. Compensation range: $144K - $335K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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