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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 AI Workflow Architect is the person who turns AI agents from promising demos into production workflows that Gallagher colleagues actually use every day. Reporting to the AI Application Development Manager, you will work directly with business users from various divisions to understand how work actually gets done, then design and build the agents and automations - primarily in Microsoft Copilot Studio and Power Platform, grounded in our Azure AI services - that fit into those workflows rather than around them. Beyond individual builds, you own the strategy and roadmap for operationalizing "maker" AI agents at Gallagher: how agents get evaluated before launch, how they are tested and monitored once live, what metrics get reported and to whom. You will also strategize on emerging Microsoft agent tooling such as Copilot Cowork and Microsoft Scout on how they should be adopted. This is a senior individual contributor role for someone with genuine systems thinking - the ability to see the whole path from a business process, through the agent, to the data and governance underneath it - and the communication skills to make that path clear to a business user, an engineer, or an executive in the organization.
How you'll make an impact
- Design Agent Workflows With Business Users: Sit with colleagues in corporate divisions to map how work actually gets done, identify where an agent adds real value versus where a human decision must stay, and design agent workflows that fit into existing processes and systems of record.
- Build and Productionalize Agents on Copilot Studio and Power Platform: Build agents, topics, connectors, and automations in Microsoft Copilot Studio and Power Platform (Power Automate, Power Apps, Dataverse), and take them through the full path from prototype to supported production solution with proper environments and release discipline.
- Own the Operationalization Strategy and Roadmap: Define and maintain Gallagher's roadmap for taking citizen developer AI agents to production - which business workflows come next, what has to be true before an agent goes live, what platform capability is needed to get there.
- Establish How Agents Get Evaluated and Tested: Create the evaluation and testing approach for agents before and after launch: golden question sets, groundedness and accuracy checks, regression testing on changes, red-team style probing for failure modes, and acceptance criteria business owners can sign off on.
- Define and Report the Metrics That Matter: Stand up the measurement and reporting layer for the agent portfolio - adoption, task completion, containment, accuracy, latency, cost, and business outcome - and deliver it as a regular, credible view for the AI Technology leadership team and business sponsors.
- Bridge Copilot Studio and the Azure Stack: Determine when a workflow belongs in low-code Copilot Studio and when it needs the engineering team's Azure AI Foundry / Azure OpenAI services, and design the integration points - custom connectors, APIs, Azure Functions, Azure AI Search indexes - that let the two work as one solution.
- Evaluate and Introduce Emerging Microsoft Agent Tooling: Assess emerging Microsoft agent and agentic-work capabilities, including Copilot Cowork and Microsoft Scout, run controlled pilots against real Gallagher workflows, and make clear recommendations on where they fit in our environment and where they don't.
- Build Agent Governance Into the Workflow: Apply Gallagher's IT policy - Entra ID/RBAC, DLP policies, environment strategy, data residency, audit logging, and human-in-the-loop checkpoints - as part of how agents are designed, not as a review afterward.
- Enable and Coach the Business: Create the patterns, templates, and guardrails that let business teams participate in agent building safely, and coach stakeholders on what agents are genuinely good at so expectations are set honestly.
- Communicate Across Audiences: Explain agent design decisions, evaluation results, and roadmap trade-offs in terms that business users, the engineering team, and executive sponsors can each act on.
About You
- Experience: 7-10+ years of professional experience in solution architecture, application development, or business process automation, including at least 1 year delivering AI/GenAI or conversational agent solutions into production.
- Copilot Studio and Power Platform: Hands-on, production-level experience with Microsoft Copilot Studio and the Power Platform (Power Automate, Power Apps, Dataverse, custom connectors), including environment strategy and deployment.
- Azure Understanding: Solid working understanding of the Azure ecosystem relevant to AI workloads - Azure OpenAI/AI Foundry, Azure AI Search, Azure Functions, API Management, Entra ID and RBAC - enough to design integrations and hold a credible architecture conversation with the engineering team.
- Systems Thinking: Demonstrated ability to reason about end-to-end systems - business process, agent behavior, integrations, data, and governance - and to anticipate second-order effects of a design rather than optimizing one component in isolation.
- Business Partnership: Track record working directly with non-technical business users to elicit how work actually gets done and translate that into a working solution they adopt.
- Evaluation and Measurement: Experience defining how a system's quality is measured and proven - test strategy, acceptance criteria, and operational metrics - rather than judging quality by demo.
- Communication: Exceptional verbal and written communication skills, with the ability to present comfortably to both engineering audiences and senior business leaders.
- Education: Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field, or equivalent practical experience.
Preferred Qualifications
- Microsoft AI Ecosystem Depth: Familiarity with M365 Copilot extensibility, Copilot agents in Teams and SharePoint, Semantic Kernel, LangChain, or Microsoft Agent Framework, and emerging Microsoft agentic-work tooling such as Copilot Cowork and Microsoft Scout.
- RAG and Grounding: Practical understanding of how content structure, metadata, chunking, and index design affect retrieval quality and agent grounding.
- Regulated Industry Experience: Insurance, financial services, or another regulated industry, with exposure to model risk management or SOX-type controls.
- Center of Excellence Experience: Experience standing up a Power Platform or automation CoE - governance model, environment and DLP strategy, reusable patterns, and maker enablement.
- Technical Depth: Working proficiency in Python, C#/.NET, or JavaScript for the cases where low-code isn't enough, plus REST API and JSON fluency.
- Delivery Tooling: Experience with Azure DevOps for backlog, pipelines, and solution deployment.
- Certifications: Microsoft certifications such as AB-100, PL-400, AI-103, or AZ-305.
Core Competencies
- Systems Thinker: Sees the whole workflow - process, agent, data, controls, and the people using it - and designs for the seams where things actually break.
- User-Obsessed: Measures success by whether colleagues adopt the agent and finish their work faster, not by whether it shipped.
- Translator: Moves fluently between a business user describing their process, an engineer debating an integration pattern, and an executive asking what it's worth.
- Pragmatic About Tooling: Picks low-code or pro-code on the merits of the problem, and is willing to say when an agent is the wrong answer.
- Evidence-Driven: Insists on evaluation results and metrics before declaring an agent ready, and reports them honestly when they're unflattering.
- Comfortable in a Regulated Environment: Treats governance, auditability, and human-in-the-loop design as part of good architecture rather than as friction.
Technology Stack
- Microsoft Copilot Studio - primary agent build and orchestration surface
- Power Platform (Power Automate, Power Apps, Dataverse, custom connectors) - workflow automation and business-facing apps
- Azure AI Foundry / Azure OpenAI Service - model and orchestration services behind pro-code agents
- Azure AI Search - retrieval and grounding for agent knowledge
- Azure Functions / API Management - integration layer between agents and line-of-business systems
- Microsoft Entra ID / Azure RBAC / DLP policies - identity, access, and data protection for agents
- M365 Copilot / Microsoft Teams - primary deployment surfaces for Gallagher colleagues
- Copilot Cowork / Microsoft Scout - emerging agentic-work tooling to evaluate and adopt
- Azure DevOps - backlog, pipelines, and solution release management
Compensation and benefits
At Gallagher, we believe supporting our colleagues goes far beyond the role itself. For more information, visit our Benefits page.* Competitive compensation
- Comprehensive benefits programs designed to support your well-being
- Career development opportunities and ongoing learning
- A collaborative, people-first culture with accessible leadership
- The opportunity to do meaningful work with global reach and local impact
At Gallagher, we are dedicated to building an inclusive and authentic workplace. If your past experience doesn’t align perfectly, we encourage you to join our Talent Community to stay connected to additional career opportunities. At times, we will consider transferable skills from previous roles.Gallagher is an affirmative action/equal opportunity employer (Minorities/Females/Veterans/Disabled)
Salary Context
This $102K-$199K range is below the median 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 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($150K) sits 29% below the category median. Disclosed range: $102K to $199K.
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.
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: $199K - $199K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 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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