AI Application Development Manager

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 Application Development Manager leads the team of developers who design, build, and maintain AJ Gallagher's AI applications — RAG pipelines, AI agents, and Copilot integrations delivered on our Microsoft Azure cloud stack. Reporting to the Director of AI Technology, you will own the day-to-day management of a team of AI Developers: setting priorities, coaching engineers of varying experience, and making sure the team ships reliable, production-grade software on schedule. You will build and maintain relationships with business stakeholders across Gallagher to understand their priorities and translate them into a delivery plan, while working within the architecture and technical standards set by the Senior AI Engineering Leads and Solution Architects. You won't be the one writing the code day to day, but you need enough fluency in AI/GenAI concepts (RAG, agents, LLMs, evaluations) and Azure infrastructure - to evaluate trade-offs, ask sharp questions, and represent your team credibly in an environment where governance and reliability matter as much as speed.

How you'll make an impact

• Lead and Develop the AI Development Team: Manage the day-to-day work of a team of AI Developers building AI systems — assigning work, coaching engineers of varying experience levels, and owning performance management for the team.
• Own Business Stakeholder Relationships: Develop and maintain relationships with business stakeholders across Gallagher to understand their priorities, then translate those priorities into a concrete plan for the team.
• Plan and Deliver AI Initiatives: Produce detailed project schedules and resource plans with project managers and manage the team's roadmap so that AI initiatives ship on time and within scope.
• Direct Delivery Within the Team's Technical Standards: Ensure the team's day-to-day build work follows the architecture, patterns, and engineering standards set by the Senior AI Engineering Lead, coordinating integration across components and modules and holding the team to the team's quality bar.
• Own Production Support for AI Applications: Be accountable for the reliability and support of every AI application and agent already in production – new features, upgrades, incident responses.
• Manage Releases and Deployments: Oversee the release and deployment process for the team's AI solutions through the team's Azure DevOps CI/CD pipelines, working with engineering to keep deployments predictable and low-risk.
• Troubleshoot and Escalate Complex Issues: Operationally troubleshoot complex production issues alongside the team, and know when to pull in the Senior AI Engineering Lead or architects for issues that require a deeper technical fix.
• Ensure Governance, Compliance, and Business Continuity: Hold the team accountable to AJ Gallagher's IT policy and responsible AI requirements - Entra ID/RBAC, audit logging, human-in-the-loop checkpoints - and develop and maintain documented standards of built systems.
• Communicate Up and Across: Give the Director of AI Technology an accurate, current picture of delivery status and risk, and translate technical trade-offs into terms business stakeholders and executives can act on.

About You

• Experience: 7-10+ years of professional software engineering experience, including at least 2 years working on AI/GenAI or ML-integrated systems (production RAG pipelines, agents, or LLM-powered applications).