AI Solution Architect

Introduction

At Gallagher, we help clients face risk with confidence because we believe that when businesses are protected, they’re free to grow, lead, and innovate. You’ll be backed by our digital ecosystem: a client-centric suite of consulting tools making it easier for you to meet your clients where they want to be met. Advanced data and analytics providing a comprehensive overview of the risk landscape is at your fingertips. Here, you’re not just improving clients' risk profiles, you’re building trust. You’ll find a culture grounded in teamwork, guided by integrity, and fueled by a shared commitment to do the right thing. We value curiosity, celebrate new ideas, and empower you to take ownership of your career while making a meaningful impact for the businesses we serve. If you’re ready to bring your unique perspective to a place where your work truly matters; think of Gallagher.

Overview

At Gallagher, we’re innovative thinkers and service-minded individuals, united by a commitment to one another’s growth and the pursuit of shared goals. As an AI Solution Architect, you’ll lead the design of AI-enabled solutions that are secure, scalable, and responsible. You’ll work closely with teams across the business to shape solutions that make a real difference.

How you'll make an impact

In this role, you’ll lead the architecture of AI solutions from idea to production. You’ll collaborate with engineers and business experts to design integrations across enterprise systems, APIs, and data workflows. You’ll also evaluate AI models for quality, security, and business outcomes, ensuring they meet clear acceptance criteria.

You’ll play a key role in applying Gallagher’s Responsible AI principles, designing systems with privacy, security, and human oversight at their core. By coaching teams and creating reusable architecture patterns, you’ll help shape the future of AI at Gallagher.

About you

Here’s what you’ll bring to the role:

• Experience in solution architecture, AI architecture, or data architecture.
• Hands-on experience delivering AI or machine-learning solutions into production.
• Knowledge of large language models (LLMs), retrieval-augmented generation (RAG), and AI evaluation techniques.
• Familiarity with APIs, cloud architecture, data platforms, and identity management.
• The ability to explain complex AI decisions clearly to both technical and non-technical audiences.
• A balanced approach to assessing business value, risk, quality, and cost.

It’s a bonus if you have experience with Microsoft Azure, Azure AI services, or insurance and financial services.