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 Release Manager owns how change and releases move through AJ Gallagher's AI portfolio. Reporting to the Director of AI Technology, you will manage change requests across every AI project and production system the team runs - RAG pipelines, Azure AI Foundry and Copilot Studio agents, and M365 Copilot integrations - and plan, coordinate, and oversee the releases that carry those changes into production. AI systems change in ways traditional applications don't: a model version is retired, a prompt is reworded, a knowledge source is re-indexed, an agent gains a new tool - and any of these can change what users see without a line of application code changing. You will make sure each of those changes is assessed, approved by the right people, released safely, and traceable, so that for any AI system in production the team can show what changed, who approved it, when it shipped, and why. Just as importantly, you will work hands-on with AI developers and the Senior AI Engineering Lead to build and improve the team's Azure DevOps release pipelines, environment promotion, and approval gates, so the governed path is also the fastest path to production. How you'll make an impact Own Change Request Intake and Lifecycle: Run intake, logging, and tracking for change requests across every AI project and production system - scope changes to in-flight initiatives as well as changes to live systems - ensuring each request states its scope, rationale, affected systems, test evidence, and rollback plan before it enters review, and following it through to release and closure. Plan and Coordinate Releases: Own the AI team's release calendar, release windows, and freeze periods; bundle approved changes into releases, sequence them across projects, run go/no-go checks, and coordinate the people involved on release day - developers, the AI Support Manager, and business owners. Help Teams Build and Improve Release Pipelines: Partner with the Senior AI Engineering Lead and AI developers to design, configure, and maintain Azure DevOps YAML pipelines, reusable pipeline templates, environment approvals and checks, and branch policies, so every team ships through a consistent, repeatable path from dev to test to production. Manage Environment Promotion: Oversee how AI solutions move between environments - Foundry agents and model deployments, Azure AI Search indexes, Azure Functions and Container Apps, and Copilot Studio solutions through Power Platform pipelines - keeping environments consistent and configuration drift under control. Bring AI-Specific Changes Under Release Control: Treat model version upgrades and Azure OpenAI model retirements, prompt and agent instruction changes, Azure AI Search index and knowledge-source changes, and new agent tools or connectors as releasable changes in their own right - and require evaluation results showing no regression in groundedness or accuracy before they are promoted to production. Assess Impact and Risk: Evaluate each change for its effect on users, downstream integrations, data access, cost, and compliance; classify it as standard, normal, or emergency and route it to the right approvers. For scope changes, quantify the effect on schedule, cost, and dependent projects, and work with the AI Business Analyst to keep requirements traceability current. Coordinate Dependencies Across Projects: Identify conflicts and dependencies between releases planned by different initiatives - shared Azure AI Search indexes, shared Foundry resources, shared connectors and APIs - and sequence them so one team's release doesn't break another's. Own Rollback, Hotfix, and Emergency Releases: Make sure every release has a tested rollback path; coordinate expedited approvals and hotfix releases with the AI Support Manager when an incident requires an urgent fix; and run post-implementation reviews on failed, rolled-back, and emergency releases, feeding what you learn back into the process and the pipelines. Publish Release Communications: Produce release notes and stakeholder notifications that tell business users what is changing in the AI tools they rely on, and give support teams what they need before a release lands. Liaise With Enterprise IT Change Management: Represent the AI team in Gallagher's enterprise IT change process, ensuring AI releases that touch shared infrastructure, identity, networking, or line-of-business systems are raised and approved through that channel as well. Report on Release Health: Produce recurring reporting for the Director of AI Technology on release cadence, lead time for changes, change failure and rollback rates, and emergency change frequency across the portfolio, and flag trends that signal delivery or quality risk. Keep the Release Record Audit-Ready: Maintain change and release records, approvals, and supporting evidence to the standard required for AI compliance reviews, model risk assessments, and internal audit - traceable from request to work items, pull requests, pipeline runs, and sign-offs without manual reconstruction - and keep the AI system inventory accurate as releases land to production. About You Experience: 4-7 years in release management, change management, or DevOps/build and release engineering, including hands-on ownership of releases and change requests in a software delivery environment. Release and Change Practice: Working knowledge of release planning and change enablement - release calendars, go/no-go criteria, risk classification, standard/normal/emergency changes, approval workflows, rollback planning, and post-implementation review. Azure DevOps Pipelines: Hands-on experience building and maintaining Azure DevOps YAML pipelines (or an equivalent such as GitHub Actions) - multi-stage pipelines, templates, environments, approvals and checks, branch policies, and variable groups - and the ability to help developers troubleshoot a failing pipeline. Azure DevOps Boards: Experience with work item queries and dashboards and linking work items to commits, pull requests, and deployments for end-to-end traceability. Technical Fluency: Able to read a pull request, pipeline log, or release note well enough to understand what a change actually does and to spot when a request is incomplete or understates its impact. Organization: Proven ability to manage a high volume of concurrent changes and releases across multiple projects and stakeholders without losing detail or letting approvals stall. Written Communication: Writes change records, release notes, and approval summaries that a non-technical approver or an auditor can act on without follow-up questions. Education: Bachelor's degree in Information Systems, Computer Science, Business, or a related field, or equivalent practical experience. Preferred Qualifications AI Exposure: Familiarity with how GenAI systems are built and changed - LLMs, RAG, agents, prompts, and evaluations - enough to recognize when a seemingly small change needs evaluation evidence before it ships. Azure Deployment Targets: Experience deploying to Azure Functions, Container Apps, or App Service, including managed identity and Key Vault-backed configuration in pipelines. Power Platform ALM: Experience with Copilot Studio solutions, Power Platform environment strategy, and Power Platform pipelines for promoting agents between environments. Infrastructure as Code: Familiarity with Terraform plan/apply workflows in pipelines, so infrastructure changes are released with the same rigor as application changes. Regulated Industry Background: Experience in insurance, financial services, or healthcare, with exposure to SOX-type change controls, internal audit and model risk management. ITSM Change Tooling: Experience with ServiceNow Change Management, Jira Service Management, or a similar platform, including CAB workflows and integration with CI/CD. Delivery Metrics: Experience measuring delivery health with DORA-style metrics - deployment frequency, lead time for changes, change failure rate, and time to restore. Certifications: AZ-400 (DevOps Engineer Expert), ITIL 4 Foundation, or AZ-900. Core Competencies Detail-Driven: Catches the missing rollback plan, the unlinked work item, or the change that quietly touches a shared index - before it reaches production. Enabler, Not Gatekeeper: Measures success by safe changes shipping faster; automates and pre-approves routine work so review effort goes where the risk actually is. Hands-On With the Tooling: Comfortable in a YAML file alongside engineers; fixes the pipeline rather than filing a ticket about it. Comfortable Saying "Not Yet": Holds the line on incomplete or under-evidenced releases, respectfully and with a clear path to approval. Systems Awareness: Understands how the pieces of the AI portfolio connect, so impact assessment reaches second-order effects rather than stopping at the component being changed. Calm on Release Day: Keeps releases, rollbacks, and hotfixes orderly when things don't go to plan. Clear Communicator: Explains what is changing and why in terms executives, auditors, business users, and engineers can each act on. Comfortable in a Regulated Environment: Treats approvals and audit trails as part of delivering trustworthy AI, not as paperwork. 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) Own Change Request Intake and Lifecycle: Run intake, logging, and tracking for change requests across every AI project and production system - scope changes to in-flight initiatives as well as changes to live systems - ensuring each request states its scope, rationale, affected systems, test evidence, and rollback plan before it enters review, and following it through to release and closure. Plan and Coordinate Releases: Own the AI team's release calendar, release windows, and freeze periods; bundle approved changes into releases, sequence them across projects, run go/no-go checks, and coordinate the people involved on release day - developers, the AI Support Manager, and business owners. Help Teams Build and Improve Release Pipelines: Partner with the Senior AI Engineering Lead and AI developers to design, configure, and maintain Azure DevOps YAML pipelines, reusable pipeline templates, environment approvals and checks, and branch policies, so every team ships through a consistent, repeatable path from dev to test to production. Manage Environment Promotion: Oversee how AI solutions move between environments - Foundry agents and model deployments, Azure AI Search indexes, Azure Functions and Container Apps, and Copilot Studio solutions through Power Platform pipelines - keeping environments consistent and configuration drift under control. Bring AI-Specific Changes Under Release Control: Treat model version upgrades and Azure OpenAI model retirements, prompt and agent instruction changes, Azure AI Search index and knowledge-source changes, and new agent tools or connectors as releasable changes in their own right - and require evaluation results showing no regression in groundedness or accuracy before they are promoted to production. Assess Impact and Risk: Evaluate each change for its effect on users, downstream integrations, data access, cost, and compliance; classify it as standard, normal, or emergency and route it to the right approvers. For scope changes, quantify the effect on schedule, cost, and dependent projects, and work with the AI Business Analyst to keep requirements traceability current. Coordinate Dependencies Across Projects: Identify conflicts and dependencies between releases planned by different initiatives - shared Azure AI Search indexes, shared Foundry resources, shared connectors and APIs - and sequence them so one team's release doesn't break another's. Own Rollback, Hotfix, and Emergency Releases: Make sure every release has a tested rollback path; coordinate expedited approvals and hotfix releases with the AI Support Manager when an incident requires an urgent fix; and run post-implementation reviews on failed, rolled-back, and emergency releases, feeding what you learn back into the process and the pipelines. Publish Release Communications: Produce release notes and stakeholder notifications that tell business users what is changing in the AI tools they rely on, and give support teams what they need before a release lands. Liaise With Enterprise IT Change Management: Represent the AI team in Gallagher's enterprise IT change process, ensuring AI releases that touch shared infrastructure, identity, networking, or line-of-business systems are raised and approved through that channel as well. Report on Release Health: Produce recurring reporting for the Director of AI Technology on release cadence, lead time for changes, change failure and rollback rates, and emergency change frequency across the portfolio, and flag trends that signal delivery or quality risk. Keep the Release Record Audit-Ready: Maintain change and release records, approvals, and supporting evidence to the standard required for AI compliance reviews, model risk assessments, and internal audit - traceable from request to work items, pull requests, pipeline runs, and sign-offs without manual reconstruction - and keep the AI system inventory accurate as releases land to production. Experience: 4-7 years in release management, change management, or DevOps/build and release engineering, including hands-on ownership of releases and change requests in a software delivery environment. Release and Change Practice: Working knowledge of release planning and change enablement - release calendars, go/no-go criteria, risk classification, standard/normal/emergency changes, approval workflows, rollback planning, and post-implementation review. Azure DevOps Pipelines: Hands-on experience building and maintaining Azure DevOps YAML pipelines (or an equivalent such as GitHub Actions) - multi-stage pipelines, templates, environments, approvals and checks, branch policies, and variable groups - and the ability to help developers troubleshoot a failing pipeline. Azure DevOps Boards: Experience with work item queries and dashboards and linking work items to commits, pull requests, and deployments for end-to-end traceability. Technical Fluency: Able to read a pull request, pipeline log, or release note well enough to understand what a change actually does and to spot when a request is incomplete or understates its impact. Organization: Proven ability to manage a high volume of concurrent changes and releases across multiple projects and stakeholders without losing detail or letting approvals stall. Written Communication: Writes change records, release notes, and approval summaries that a non-technical approver or an auditor can act on without follow-up questions. Education: Bachelor's degree in Information Systems, Computer Science, Business, or a related field, or equivalent practical experience. Preferred Qualifications AI Exposure: Familiarity with how GenAI systems are built and changed - LLMs, RAG, agents, prompts, and evaluations - enough to recognize when a seemingly small change needs evaluation evidence before it ships. Azure Deployment Targets: Experience deploying to Azure Functions, Container Apps, or App Service, including managed identity and Key Vault-backed configuration in pipelines. Power Platform ALM: Experience with Copilot Studio solutions, Power Platform environment strategy, and Power Platform pipelines for promoting agents between environments. Infrastructure as Code: Familiarity with Terraform plan/apply workflows in pipelines, so infrastructure changes are released with the same rigor as application changes. Regulated Industry Background: Experience in insurance, financial services, or healthcare, with exposure to SOX-type change controls, internal audit and model risk management. ITSM Change Tooling: Experience with ServiceNow Change Management, Jira Service Management, or a similar platform, including CAB workflows and integration with CI/CD. Delivery Metrics: Experience measuring delivery health with DORA-style metrics - deployment frequency, lead time for changes, change failure rate, and time to restore. Certifications: AZ-400 (DevOps Engineer Expert), ITIL 4 Foundation, or AZ-900. Core Competencies Detail-Driven: Catches the missing rollback plan, the unlinked work item, or the change that quietly touches a shared index - before it reaches production. Enabler, Not Gatekeeper: Measures success by safe changes shipping faster; automates and pre-approves routine work so review effort goes where the risk actually is. Hands-On With the Tooling: Comfortable in a YAML file alongside engineers; fixes the pipeline rather than filing a ticket about it. Comfortable Saying "Not Yet": Holds the line on incomplete or under-evidenced releases, respectfully and with a clear path to approval. Systems Awareness: Understands how the pieces of the AI portfolio connect, so impact assessment reaches second-order effects rather than stopping at the component being changed. Calm on Release Day: Keeps releases, rollbacks, and hotfixes orderly when things don't go to plan. Clear Communicator: Explains what is changing and why in terms executives, auditors, business users, and engineers can each act on. Comfortable in a Regulated Environment: Treats approvals and audit trails as part of delivering trustworthy AI, not as paperwork.
