Interested in this AI/ML Engineer role at Gallagher?
Apply Now →Skills & Technologies
About This Role
Introduction
Welcome to Gallagher \- a global community of people who bring bold ideas, deep expertise, and a shared commitment to doing what’s right. We help clients navigate complexity with confidence by empowering businesses, communities, and individuals to thrive. At Gallagher, you’ll find more than a job; you’ll find a culture built on trust, driven by collaboration, and sustained by the belief that we’re better together. Whether you join us in a client\-facing role or as part of our brokerage division, our benefits and HR consulting division, or our corporate team, you’ll have the opportunity to grow your career, make an impact, and be part of something bigger. Experience a workplace where you’re encouraged to be yourself, supported to succeed, and inspired to keep learning. That’s what it means to live The Gallagher Way.
Overview
The Senior AI Engineering Lead owns the technical delivery of enterprise AI solutions at AJ Gallagher. Reporting to the Director of AI, you will lead the design, build, and deployment of production AI systems \- RAG pipelines, agentic workflows, and Copilot integrations \- on our Azure and .NET/Python stack. You will set engineering standards for a growing AI team, mentor developers, and partner directly with business units to turn ambiguous problems into shipped, governed, production\-grade AI products in a regulated insurance environment.
How you'll make an impact
- Translate Business Problems into Technical Solutions: Work side\-by\-side with business leaders and subject\-matter experts to unpack ambiguous, unstructured challenges and shape them into well\-scoped, buildable AI solutions with a clear delivery path.
- Own AI Solution Architecture: Design and defend end\-to\-end architectures for GenAI and agentic systems on Microsoft Foundry \- spanning Azure OpenAI, Azure AI Search, Foundry Tools, and Copilot Studio \- that meet enterprise security, scalability, and data\-residency requirements.
- Lead Hands\-On Delivery: Write and review production code (C\#/.NET and Python) for RAG pipelines, AI agent orchestration, and API integrations; remain a strong individual contributor while leading the team.
- Set Engineering Standards: Establish patterns for agentic development, evaluation, testing, CI/CD (Azure DevOps), and observability so AI solutions are repeatable and maintainable \- not one\-off prototypes.
- Mentor and Grow Engineers: Coach AI developers through design reviews, pairing, and career development; raise the technical bar across the team.
- Ship to Enterprise Surfaces: Deliver AI capabilities into internal applications, Microsoft Teams, and M365 Copilot, integrating through Foundry agents, Service Bus, Azure Functions, and other cloud\-native services.
- Build Governance In: Implement responsible\-AI controls \- Entra ID/RBAC, IaC Policy, human\-in\-the\-loop checkpoints, audit logging \- aligned to Gallagher compliance expectations from day one, not as an afterthought.
- Drive Evaluation and Reliability: Define measurable quality bars (groundedness, accuracy, latency, cost) and build the evaluation harnesses to enforce them before and after release.
- Communicate Up and Across: Translate technical trade\-offs into clear recommendations for the Director of AI and business stakeholders; manage delivery risks and dependencies proactively.
About You
- Experience: 7 or more years in software engineering, including experience in building LLM/GenAI systems in production and 2\+ years leading engineers or owning technical direction.
- Azure Depth: Hands\-on production experience with Microsoft Foundry, Azure OpenAI Service, Azure AI Search, and Azure compute (Functions, Container Apps, or App Service).
- Languages: Strong .NET/C\# plus working proficiency in Python; able to review and contribute in both.
- GenAI Engineering: Proven delivery of RAG architectures, agent orchestration (Microsoft Agent Framework, Semantic Kernel, or LangChain), and prompt/evaluation pipelines.
- API and Integration Skills: Designing secure REST APIs and event\-driven integrations (Azure API Management, Service Bus) within an enterprise identity model (Entra ID, RBAC).
- Delivery Discipline: Agile experience with Azure DevOps (or equivalent) \- backlogs, CI/CD, automated testing, and release management.
- Leadership: Track record of mentoring engineers, running design reviews, and owning technical decisions across multiple concurrent initiatives.
Preferred Differentiators
=============================
- Regulated Industry Experience: Prior AI/ML delivery in insurance, financial services, or healthcare, with familiarity with model risk management or responsible AI guidance.
- Familiarity with AI\-enabled and spec\-driven development: Using AI\-assisted tooling and agent workflows across the SDLC to quickly deliver production\-grade systems.
- Experience with Infrastructure\-as\-Code: Building applications tightly aligned to Terraform\-deployed, optimized infrastructure.
- Evaluation and MLOps Maturity: Built LLMOps tooling \- automated evals, red\-teaming, drift/cost monitoring \- at enterprise scale.
- Architecture Credentials: Azure certifications (AZ\-305, AI\-103\) or equivalent demonstrated architecture ownership.
Professional Qualities
==========================
- Bias for Shipped Outcomes: Measures success by working software in users' hands, not demos or decks; cuts scope intelligently to ship.
- Calm Technical Authority: Makes and defends decisions under ambiguity, and changes course quickly when evidence demands it.
- Force Multiplier: Gets more from the team than from their own keyboard \- through mentoring, standards, and unblocking others.
- Governance as a Feature: Treats compliance, security, and auditability as design inputs that build trust, not friction to route around.
- Clear Communicator: Explains complex systems simply to executives and precisely to engineers; writes things down.
- Pragmatic Curiosity: Tracks the fast\-moving AI landscape but adopts new tools only when they solve a real AJ Gallagher problem.
Compensation and benefits
We offer a competitive and comprehensive compensation package. The base salary range represents the anticipated low end and high end of the range for this position. The actual compensation will be influenced by a wide range of factors including, but not limited to previous experience, education, pay market/geography, complexity or scope, specialized skill set, lines of business/practice area, supply/demand, and scheduled hours. On top of a competitive salary, great teams and exciting career opportunities, we also offer a wide range of benefits.
Below are the minimum core benefits you’ll get, depending on your job level these benefits may improve:
- Medical/dental/vision plans, which start from day one!
- Life and accident insurance
- 401(K) and Roth options
- Tax\-advantaged accounts (HSA, FSA)
- Educational expense reimbursement
- Paid parental leave
Other benefits include:* Digital mental health services (Talkspace)
- Flexible work hours (availability varies by office and job function)
- Training programs
- Gallagher Thrive program – elevating your health through challenges, workshops and digital fitness programs for your overall wellbeing
- Charitable matching gift program
- And more...
*\*\*The benefits summary above applies to fulltime positions. If you are not applying for a fulltime position, details about benefits will be provided during the selection process.* We value inclusion and diversity
Click Here to review our U.S. Eligibility Requirements
Inclusion and diversity (I\&D) is a core part of our business, and it’s embedded into the fabric of our organization. For more than 95 years, Gallagher has led with a commitment to sustainability and to support the communities where we live and work.
Gallagher embraces our employees’ diverse identities, experiences and talents, allowing us to better serve our clients and communities. We see inclusion as a conscious commitment and diversity as a vital strength. By embracing diversity in all its forms, we live out The Gallagher Way to its fullest.
Gallagher believes that all persons are entitled to equal employment opportunity and prohibits any form of discrimination by its managers, employees, vendors or customers based on race, color, religion, creed, gender (including pregnancy status), sexual orientation, gender identity (which includes transgender and other gender non\-conforming individuals), gender expression, hair expression, marital status, parental status, age, national origin, ancestry, disability, medical condition, genetic information, veteran or military status, citizenship status, or any other characteristic protected (herein referred to as “protected characteristics”) by applicable federal, state, or local laws.
Equal employment opportunity will be extended in all aspects of the employer\-employee relationship, including, but not limited to, recruitment, hiring, training, promotion, transfer, demotion, compensation, benefits, layoff, and termination. In addition, Gallagher will make reasonable accommodations to known physical or mental limitations of an otherwise qualified person with a disability, unless the accommodation would impose an undue hardship on the operation of our business.
Salary Context
This $119K-$233K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Gallagher, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($176K) sits 19% below the category median. Disclosed range: $119K to $233K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Gallagher AI Hiring
Gallagher has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Rolling Meadows, IL, US. Compensation range: $233K - $233K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.