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About This Role
Huron is redefining what a global consulting organization can be. Advancing new ideas every day to build even stronger clients, individuals and communities. We’re helping our clients find new ways to drive growth, enhance business performance and sustain leadership in the markets they serve. And, we’re developing strategies and implementing solutions that enable the transformative change they need to own their future.
As a member of the Huron corporate team, you’ll help to evolve our business model to stay ahead of market forces, industry trends and client needs. Our accounting, finance, human resources, IT, legal, marketing and facilities management professionals work collaboratively to support Huron’s collective strategies and enable real transformation to produce sustainable business results.
Join our team and create your future.
Role Summary
The Director, AI Infrastructure Operations will lead the AI Operations team and run the day-to-day operations of Huron's governed AI capability within Corporate IT. This role combines operational leadership and people management with practical platform understanding: managing the team, the intake and prioritization process, the decision forums, and the delivery of the AI Operations roadmap.
The role is designed to help teams move faster through appropriate governance by operating approved pathways, clear decision rights, reusable patterns, evidence standards, and measurable adoption goals. The person in this role should be comfortable using approved AI tools to develop artifacts, evaluate options, review technical approaches, and continuously improve the operating model. The capability is centered on AI infrastructure and platform operations rather than application development, though the team supports and contributes to solution delivery where needed.
Reporting / Organization
This role sits within AI Infrastructure Operations in Corporate IT, part of the CIO organization, with the CIO several levels above this role in the reporting line. It manages the AI Infrastructure Operations team, including the AI Infrastructure Architect and AI Infrastructure Engineer roles. The executive sponsor for the capability is the CIO.Key Responsibilities
- Hire, develop, and manage the AI Infrastructure Operations team, including architects and engineers; set goals, provide coaching and performance feedback, and build team capability over time.
- Own day-to-day execution of the AI Infrastructure Operations charter: roadmap delivery, prioritization, intake process, decision forums, adoption outcomes, and operating cadence.
- Operate the governed enterprise AI capability, including workload classes, priority use cases, Bedrock access needs, usage and cost visibility, sandbox ownership, application and agent registration, logging, exception handling, Huron Knowledge validation, and decision rights.
- Run the federated platform operating model in partnership with Client-facing AI Delivery, Enterprise IT, Global Products, Engineering, Analytics, Consulting, Security, Privacy, Compliance, Legal, Finance, and Enterprise Architecture.
- Partner with the AI Capability Center under the Chief AI Officer to align priorities, innovate, and deliver the infrastructure and platform capabilities behind solutions for Huron's go-to-market teams.
- Provide enough technical direction, working with the AI Infrastructure Architect, to make architecture, sequencing, control-plane, and delivery tradeoffs visible and actionable.
- Use AI tools to accelerate planning, documentation, backlog development, decision records, stakeholder communication, and review of platform artifacts.
- Ensure AI Operations is positioned as an enablement capability that provides reusable, approved, observable, and cost-managed paths for AI adoption.
- Manage scope, stakeholder participation, budget tracking, and scaling criteria across delivery phases.
- Track and report success measures for governed access adoption, workload visibility, cost visibility, operational readiness, Huron Knowledge enablement, agent safety, and delivery acceleration.
Required Qualifications
- 10+ years of experience in enterprise infrastructure, platform leadership, cloud operations, AI enablement, digital transformation, or enterprise architecture, including 3+ years directly managing technical teams or leading cross-functional platform work.
- Demonstrated people management experience: hiring, developing, coaching, and performance management of technical staff.
- Strong understanding of enterprise AI operating needs, including model access, data protection, cost attribution, auditability, workload classification, sandboxing, governed knowledge, and AI governance.
- Technical fluency with cloud platforms, AI platforms, platform engineering, or enterprise architecture.
- Demonstrated ability to use AI tools as a practical system-building accelerator for analysis, documentation, planning, technical review, or lightweight prototyping.
- Ability to translate technical platform work into business outcomes, stakeholder decisions, funding needs, and adoption measures.
- Proven experience working across senior stakeholders in technology, security, privacy, compliance, finance, legal, architecture, product, and delivery organizations.
- Strong written communication skills, including decision memos, roadmap artifacts, and operating model documentation.
Preferred Qualifications
- Experience with AWS, Amazon Bedrock, enterprise AI platforms, MLOps, LLMOps, platform engineering, or cloud governance.
- Experience with durable agentic AI orchestration platforms for platform, business, or AI operational workflows.
- Experience creating or operating intake, prioritization, governance, exception, or architecture review processes.
- Experience with regulated, client-sensitive, PHI, PII, or confidential-data environments.
- Experience leading platform adoption across federated business teams.
Flexible living locations across the US. Ability to travel as needed.
The estimated base salary for this job is $190,000 - $265,000. The actual salary paid to an individual will vary based on multiple factors, including but not limited to specific skills or certifications, years of experience, market changes, and required travel. This job is also eligible to participate in Huron’s annual incentive compensation program, which reflects Huron’s pay for performance philosophy. Inclusive of annual incentive compensation opportunity, the total estimated compensation range for this job is $247,000 - $344,500. The job is also eligible to participate in Huron’s benefit plans which include medical, dental and vision coverage and other wellness programs.
Position Level
DirectorCountry
United States of America
Salary Context
This $190K-$344K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Huron Consulting Group, 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($267K) sits 26% above the category median. Disclosed range: $190K to $344K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Huron Consulting Group AI Hiring
Huron Consulting Group has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $344K - $344K.
Location Context
AI roles in Chicago pay a median of $194,700 across 390 tracked positions. That's 9% below the national 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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