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About This Role
Huron helps its clients drive growth, enhance performance and sustain leadership in the markets they serve. We help healthcare organizations build innovation capabilities and accelerate key growth initiatives, enabling organizations to own the future, instead of being disrupted by it. Together, we empower clients to create sustainable growth, optimize internal processes and deliver better consumer outcomes.
Health systems, hospitals and medical clinics are under immense pressure to improve clinical outcomes and reduce the cost of providing patient care. Investing in new partnerships, clinical services and technology is not enough to create meaningful and substantive change. To succeed long-term, healthcare organizations must empower leaders, clinicians, employees, affiliates and communities to build cultures that foster innovation to achieve the best outcomes for patients.
Joining the Huron team means you’ll help our clients evolve and adapt to the rapidly changing healthcare environment and optimize existing business operations, improve clinical outcomes, create a more consumer-centric healthcare experience, and drive physician, patient and employee engagement across the enterprise.
Join our team as the expert you are now and create your future.
Market Insights is Huron's analytics product suite, built on a foundation of tens of billions of medical claims, millions of clinical providers, and hundreds of millions of unique patient lives. We are looking for a Full Stack Lead Engineer to guide the technical direction of this platform end to end, from the data layer through the user interface, with a mandate to embed AI directly into how our analytics products are built and how they deliver insight to clients.
This is a hands on leadership role. You will write and review code, set architecture and technical standards, and lead a team of engineers, while partnering closely with product management, data engineering, and data science to bring intelligent, AI enabled analytics capabilities to market faster.Responsibilities
Full stack product development
- Lead design and development across the stack: responsive front end interfaces, APIs and services, and the underlying data layer for analytics products.
- Set technical direction and architecture for scalability, security, and performance of AWS based analytics solutions.
- Collaborate with product management on roadmap, prioritization, and feature delivery using Agile practices.
AI and machine learning in analytics
- Identify and lead opportunities to embed AI and Agentic AI capabilities into analytics products: predictive models, natural language interfaces over data, automated insight generation, and generative AI features that help clients act faster on their data.
- Partner with data scientists to move machine learning models from prototype into reliable, production grade services.
- Evaluate and apply large language model based tools where they add measurable client value, with attention to data privacy, accuracy, and healthcare compliance requirements.
Data engineering
- Design and maintain ETL and data pipeline processes that feed dashboards, models, and client facing reporting.
- Work with cloud data services, including S3, Redshift, Glue, Lambda, and Athena, to build scalable data infrastructure.
- Uphold data governance, quality, and healthcare data handling standards across the platform.
Team leadership
- Support, and mentor a team of engineers, providing technical guidance and career development.
- Establish and continuously improve engineering practices, code quality standards, and delivery processes.
- Communicate progress, risks, and technical decisions clearly to product leadership and cross functional stakeholders.
Qualifications
Required:
- 8 or more years of professional software engineering experience, including full stack development across both front end and back end technologies.
- 3 or more years leading or mentoring engineers, formally or informally.
- Demonstrated experience applying AI or machine learning within a production analytics or data product, such as predictive modeling, recommendation systems, or generative AI features.
- Strong front end development skills in TypeScript, JavaScript, and React.
- Strong back end skills in Python, and an equivalent language used for analytics and API development.
- Solid understanding of relational databases and SQL.
- Working knowledge of core AWS services relevant to data and analytics, including Lambda, IAM, S3, Redshift, Glue, and Athena.
- Experience with infrastructure as code using AWS CloudFormation and the AWS CDK.
- Experience with version control systems, particularly Git.
- Experience delivering software using Agile methodologies.
- Excellent written and verbal communication skills, with the ability to explain technical tradeoffs to non-technical stakeholders.
Preferred:
- Familiarity with healthcare data interoperability standards, such as ANSI X12, HL7, and FHIR.
- Experience with healthcare claims data is a plus.
- Hands on experience with large language models, prompt engineering, retrieval augmented generation, or vector databases.
- Experience with MLOps practices for deploying and monitoring machine learning models in production.
- Experience with SQLAlchemy and PostgreSQL.
- Experience with row based databases such as Amazon RDS and Aurora.
- Experience with columnar or data warehouse platforms such as Redshift, Athena, and Snowflake.
- Experience with NoSQL databases such as DynamoDB.
- Experience with a broader range of AWS services beyond the core set, and with Azure DevOps for build and release pipelines.
- Experience with big data technologies such as Spark, or infrastructure as code tools such as Terraform.
- Background in healthcare or higher education analytics.
- AWS certification, such as AWS Certified Solutions Architect or AWS Certified Developer.
- A bachelor's degree in computer science, engineering, or a related field, or equivalent practical experience.
Position Level
Senior AssociateCountry
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 9,653 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. Senior-level AI roles across all categories have a median of $228,700.
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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