AI Development Engineer Senior

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.

• 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.

• 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

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)