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About This Role
Huron helps its clients drive growth, enhance performance and sustain leadership in the markets they serve. We collaborate with education organizations to develop strategies and implement solutions that enable the transformative change our clients need to own their future. Together, we empower clients to drive innovation, create sustainable funding streams and deliver better student outcomes.
Today, leaders of higher education institutions and academic medical centers are spending too much time reacting to market forces, rather than focusing on the essential priorities that help students, faculty and staff thrive. The most productive path forward requires an intentional approach and innovative thinking, whereby stakeholders across the entire institution rally around a shared vision and embrace the hard work of effecting change.
You’ll help our clients to achieve organizational effectiveness, improve student outcomes, implement new technologies and align resources and investments to ensure long-term sustainability.
Join our team as the expert you are now and create your future.
The Lead Platform Engineer is a Manager-level role responsible for leading the architecture, deployment, and operationalisation of Huron's AI, Microsoft Fabric, and workflow platforms. Responsible for designing, building, and maintaining the core platforms and tooling that power our Managed Services delivery across 50+ clients. This role sits at the intersection of platform engineering, client deployment, and AI workflow development, serving as a key technical owner for our internal tooling ecosystem.
The ideal candidate brings hands-on experience across modern DevOps toolchains, CRM and workflow platforms, cloud data environments, and emerging AI technologies. They are comfortable operating independently within a fast-paced professional services environment while collaborating with internal stakeholders, development teams, and client organizations.Key Responsibilities
- Own the architecture, configuration, standardization, and ongoing administration of internal Azure DevOps (ADO) environments used to manage work across multiple client engagements.
- Serve as the primary liaison to the Huron Corporate Salesforce Development Team, coordinating requirements gathering, backlog prioritization, sprint planning input, user acceptance testing (UAT), and deployment activities for enhancements to the Salesforce workflow platform supporting Managed Services delivery.
- Lead the design, development, and maintenance of internal AI-powered solutions and Claude AI projects, including prompt workflows, automation pipelines, prototypes, and scalable processes that embed AI capabilities into day-to-day operations.
- Coordinate client onboarding and implementation activities related to Microsoft Fabric deployments, partnering with client IT and data teams to configure environments, establish reporting foundations, and support data integration requirements.
- Collaborate with platform, analytics, and reporting resources to provide technical architecture guidance, review Power BI data models, and assess dependencies across reporting and data pipeline solutions.
- Support the expansion of Managed Services operational processes into Salesforce and other enterprise platforms, helping define workflows, coordinate deployments, and document implementation approaches.
- Develop and maintain technical documentation, platform standards, operational runbooks, and knowledge-sharing materials that support consistency, scalability, and operational resilience across the Managed Services organization.
- Participate in the internal Tech Committee, providing senior technical leadership, evaluating emerging technologies, and mentoring team members on platform engineering and automation best practices.
Required Qualifications
- 5+ years of experience in a platform engineering, DevOps, cloud operations, or technical systems role, preferably within a professional services, consulting, or managed services environment.
- Hands-on experience administering Azure DevOps (ADO), including pipeline configuration, work item management, governance, reporting, and multi-project administration.
- Working knowledge of Salesforce administration, platform capabilities, or development coordination, including experience managing enhancement requests and collaborating with development teams.
- Demonstrated experience with Microsoft Fabric or related cloud data platforms such as Azure Data Factory, Azure Synapse Analytics, Databricks, or similar technologies, including implementation or client onboarding activities.
- Experience designing or supporting AI and large language model (LLM) solutions, including prompt engineering, workflow automation, AI tool adoption, or integrations leveraging Claude, OpenAI, or comparable platforms.
- Strong written and verbal communication skills with the ability to translate technical concepts for business stakeholders, end users, and client teams.
- Experience managing multiple priorities and stakeholders while working effectively across cross-functional internal and client-facing environments.
Preferred Qualifications
- Experience in a Managed Services, consulting, or client delivery organization.
- Microsoft certifications in Azure, Microsoft Fabric, Power Platform, or related technologies.
- Familiarity with Salesforce Flows, Process Builder, workflow automation, or working with system integrators and external development partners.
- Experience with Python, JavaScript, PowerShell, or other scripting languages used for automation and platform development.
- Experience evaluating, piloting, or implementing enterprise AI solutions and governance practices.
- Exposure to data visualization and reporting solutions, including Power BI architecture and administration.
- Experience contributing to technical standards, governance committees, or mentoring less experienced technical team members.
The estimated base salary range for this job is $155,000 - $175,000.The range represents a good faith estimate of the range that Huron reasonably expects to pay for this job at the time of the job posting. 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 $178,250 - $218,,750. The job is also eligible to participate in Huron’s benefit plans which include medical, dental and vision coverage and other wellness programs. The salary range information provided is in accordance with applicable state and local laws regarding salary transparency that are currently in effect and may be implemented in the future.
Position Level
ManagerCountry
United States of America
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. Senior-level AI roles across all categories have a median of $229,300.
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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