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About This Role
Huron is a global consultancy that collaborates with clients to drive strategic growth, ignite innovation and navigate constant change. Through a combination of strategy, expertise and creativity, we help clients accelerate operational, digital and cultural transformation, enabling the change they need to own their future.
Join our team as the expert you are now and create your future.
The Director, AI Advisory is the engagement leader and delivery anchor for Huron’s AI Strategy & Transformation Practice. Reporting to the Senior Director, AI Strategy, Advisory & Partnerships, this role leads AI strategy and implementation engagements, along with the 12–15 person team that delivers them. The core focus is healthcare (health systems, academic medical centers, and other provider organizations), with the reach to extend proven approaches to clients in financial services, education, and commercial industries.
The role carries three reinforcing accountabilities: sell, by shaping and winning AI advisory work alongside Senior Directors, Principals and Managing Directors; serve, by leading engagements from AI vision through measurable value realization; and build, by developing the people and intellectual capital that scale the practice. You will translate AI strategy into executable roadmaps, roadmaps into realized value, and realized value into the next engagement. Working closely with the Senior Director, you will also help shape the Practice’s growth agenda, including new offerings, strategic partnerships, and expansion into adjacent markets.KEY RESPONSIBILITIES
1. Client Engagement Leadership & Delivery
- Engagement Leader: Serve as the accountable engagement leader for AI strategy and implementation engagements — owning scope, quality, timeline, financials, and client satisfaction across multiple concurrent engagements of varying scale and duration.
- Strategy Engagements: Lead AI vision sessions, maturity diagnostics, value lens assessments, roadmap development, and AI operating model design, anchored in measurable clinical, operational, and financial outcomes.
- Value Definition: Identify and prioritize AI and intelligent automation opportunities across clinical, operational, and revenue cycle workflows; build business cases and ROI models that executives will fund.
- Solution Shaping: Guide solution design across generative and agentic AI, machine learning, and intelligent automation — including architecture, application integration, data readiness, and process redesign — so strategy translates into production-grade deployments.
- Governance & Operating Model: Advise clients on AI and automation governance, risk, and operating model structures that withstand clinical, regulatory, and Board-level scrutiny.
2. Business Development & Growth
- Pursuit Leadership: Partner with Managing Directors, Principals and Senior Directors to identify, shape, and close consultative sales opportunities — leading proposal development, pricing, statements of work, and orals.
- Account Expansion: Build trusted relationships with C-Suite client executives and clinical leaders and convert advisory engagements into broader transformation and implementation programs.
- Executive Translation: Articulate the business value of AI to executive audiences through succinct, board-grade narratives and investment cases.
- Market & Vendor Insight: Evaluate healthcare AI and automation solutions in the market; advise clients on build-versus-buy-versus-partner decisions and support joint pursuits with Huron’s ecosystem partners.
3. Offering & Practice Development
- IP & Frameworks: Codify delivery insight into reusable team playbooks across key strategy topics, including AI value capture, governance, operating model design, and agent economics.
- Thought Leadership: Contribute to points of view, whitepapers, and speaking engagements that build Huron’s voice in healthcare AI and convert market presence into pipeline.
4. Team Leadership & People Development
- Team Leadership: Lead, coach, and manage a team of 12–15 consultants — Managers, Senior Associates, Associates, and Analysts — across concurrent engagements, building a high-performance culture.
- Capability Building: Develop the team’s depth on key strategy topics and the advisory craft — problem structuring, executive communication, business case development, and client navigation.
- Talent Management: Own staffing, performance feedback, and career development for the team; contribute to recruiting and strengthening the practice bench.
- Cross-Functional Alignment: Partner with Huron’s Healthcare, Digital, and AI Capability Center teams to deliver integrated client outcomes and a single Huron voice.
THE IDEAL PROFILE
- Consulting Pedigree: 10+ years in consulting, leading enterprise AI, technology, or intelligent automation engagements at a top-tier strategy, technology, or Big Four firm in a plus.
- Healthcare Fluency: Direct experience serving healthcare providers; command of healthcare economics and AI-relevant pain points across clinical, operational, and revenue cycle workflows strongly preferred.
- AI & Automation Depth: Deep expertise defining and implementing generative and agentic AI, machine learning, RPA, and intelligent automation in enterprise production environments.
- Strategic Problem Solving: Strong structured problem solving and business analysis, with proven development of business cases, value propositions, and roadmaps that secure executive investment.
- Team Leadership: Demonstrated ability to lead and develop teams of 10+ consultants across multiple workstreams, inspiring clients, teams, and leadership alike.
- Commercial Acumen: Proven ability to identify and lead consultative sales — from origination through proposal, orals, and close — and to grow accounts through delivery excellence.
- Executive Presence: Credible in a technical conversation with a CIO and a value conversation with a CFO in the same meeting; influential in a matrixed organization.
- Growth Mindset: A builder’s orientation — able to operate with ambiguity, contribute to net-new offerings, and convert white space into a repeatable practice motion.
ROLE REQUIREMENTS
- Education: Bachelor’s degree in a related field or equivalent work experience; MBA or relevant advanced degree strongly preferred.
- Travel & Location: Willingness to travel up to 50% to support client engagements and pursuits; flexible living locations across the US and Canada.
- Certifications: Plus: AI or automation platform certifications (e.g., Azure AI, AWS, UiPath, Power Platform).
The estimated base salary for this job is $195,000 - $245,000. 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 $253,000 - $318,000. 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 $195K-$318K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 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 $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($256K) sits 21% above the category median. Disclosed range: $195K to $318K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Huron Consulting Group AI Hiring
Huron Consulting Group has 5 open AI roles right now. They're hiring across Data Engineer, AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $177K - $318K.
Location Context
AI roles in Chicago pay a median of $193,440 across 429 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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