Senior Ai Agent Jobs in Raleigh, NC (NOW HIRING)

The Senior AI Engineering Lead owns the technical delivery of enterprise AI solutions at AJ Gallagher. Reporting to the Director of AI, you will lead the design, build, and deployment of production AI systems - RAG pipelines, agentic workflows, and Copilot integrations - on our Azure and .NET/Python stack. You will set engineering standards for a growing AI team, mentor developers, and partner directly with business units to turn ambiguous problems into shipped, governed, production-grade AI products in a regulated insurance environment.

• Translate Business Problems into Technical Solutions: Work side-by-side with business leaders and subject-matter experts to unpack ambiguous, unstructured challenges and shape them into well-scoped, buildable AI solutions with a clear delivery path.
• Own AI Solution Architecture: Design and defend end-to-end architectures for GenAI and agentic systems on Microsoft Foundry - spanning Azure OpenAI, Azure AI Search, Foundry Tools, and Copilot Studio - that meet enterprise security, scalability, and data-residency requirements.
• Lead Hands-On Delivery: Write and review production code (C#/.NET and Python) for RAG pipelines, AI agent orchestration, and API integrations; remain a strong individual contributor while leading the team.
• Set Engineering Standards: Establish patterns for agentic development, evaluation, testing, CI/CD (Azure DevOps), and observability so AI solutions are repeatable and maintainable - not one-off prototypes.
• Mentor and Grow Engineers: Coach AI developers through design reviews, pairing, and career development; raise the technical bar across the team.
• Ship to Enterprise Surfaces: Deliver AI capabilities into internal applications, Microsoft Teams, and M365 Copilot, integrating through Foundry agents, Service Bus, Azure Functions, and other cloud-native services.
• Build Governance In: Implement responsible-AI controls - Entra ID/RBAC, IaC Policy, human-in-the-loop checkpoints, audit logging - aligned to Gallagher compliance expectations from day one, not as an afterthought.
• Drive Evaluation and Reliability: Define measurable quality bars (groundedness, accuracy, latency, cost) and build the evaluation harnesses to enforce them before and after release.
• Communicate Up and Across: Translate technical trade-offs into clear recommendations for the Director of AI and business stakeholders; manage delivery risks and dependencies proactively.

• Experience: 7 or more years in software engineering, including experience in building LLM/GenAI systems in production and 2+ years leading engineers or owning technical direction.
• Azure Depth: Hands-on production experience with Microsoft Foundry, Azure OpenAI Service, Azure AI Search, and Azure compute (Functions, Container Apps, or App Service).
• Languages: Strong .NET/C# plus working proficiency in Python; able to review and contribute in both.
• GenAI Engineering: Proven delivery of RAG architectures, agent orchestration (Microsoft Agent Framework, Semantic Kernel, or LangChain), and prompt/evaluation pipelines.
• API and Integration Skills: Designing secure REST APIs and event-driven integrations (Azure API Management, Service Bus) within an enterprise identity model (Entra ID, RBAC).
• Delivery Discipline: Agile experience with Azure DevOps (or equivalent) - backlogs, CI/CD, automated testing, and release management.
• Leadership: Track record of mentoring engineers, running design reviews, and owning technical decisions across multiple concurrent initiatives.

• Regulated Industry Experience: Prior AI/ML delivery in insurance, financial services, or healthcare, with familiarity with model risk management or responsible AI guidance.
• Familiarity with AI-enabled and spec-driven development: Using AI-assisted tooling and agent workflows across the SDLC to quickly deliver production-grade systems.
• Experience with Infrastructure-as-Code: Building applications tightly aligned to Terraform-deployed, optimized infrastructure.
• Evaluation and MLOps Maturity: Built LLMOps tooling - automated evals, red-teaming, drift/cost monitoring - at enterprise scale.
• Architecture Credentials: Azure certifications (AZ-305, AI-103) or equivalent demonstrated architecture ownership.

• Bias for Shipped Outcomes: Measures success by working software in users' hands, not demos or decks; cuts scope intelligently to ship.
• Calm Technical Authority: Makes and defends decisions under ambiguity, and changes course quickly when evidence demands it.
• Force Multiplier: Gets more from the team than from their own keyboard - through mentoring, standards, and unblocking others.
• Governance as a Feature: Treats compliance, security, and auditability as design inputs that build trust, not friction to route around.
• Clear Communicator: Explains complex systems simply to executives and precisely to engineers; writes things down.
• Pragmatic Curiosity: Tracks the fast-moving AI landscape but adopts new tools only when they solve a real AJ Gallagher problem.

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)

• Experience: 7 or more years in software engineering, including experience in building LLM/GenAI systems in production and 2+ years leading engineers or owning technical direction.
• Azure Depth: Hands-on production experience with Microsoft Foundry, Azure OpenAI Service, Azure AI Search, and Azure compute (Functions, Container Apps, or App Service).
• Languages: Strong .NET/C# plus working proficiency in Python; able to review and contribute in both.
• GenAI Engineering: Proven delivery of RAG architectures, agent orchestration (Microsoft Agent Framework, Semantic Kernel, or LangChain), and prompt/evaluation pipelines.
• API and Integration Skills: Designing secure REST APIs and event-driven integrations (Azure API Management, Service Bus) within an enterprise identity model (Entra ID, RBAC).
• Delivery Discipline: Agile experience with Azure DevOps (or equivalent) - backlogs, CI/CD, automated testing, and release management.
• Leadership: Track record of mentoring engineers, running design reviews, and owning technical decisions across multiple concurrent initiatives.

• Regulated Industry Experience: Prior AI/ML delivery in insurance, financial services, or healthcare, with familiarity with model risk management or responsible AI guidance.
• Familiarity with AI-enabled and spec-driven development: Using AI-assisted tooling and agent workflows across the SDLC to quickly deliver production-grade systems.
• Experience with Infrastructure-as-Code: Building applications tightly aligned to Terraform-deployed, optimized infrastructure.
• Evaluation and MLOps Maturity: Built LLMOps tooling - automated evals, red-teaming, drift/cost monitoring - at enterprise scale.
• Architecture Credentials: Azure certifications (AZ-305, AI-103) or equivalent demonstrated architecture ownership.

• Bias for Shipped Outcomes: Measures success by working software in users' hands, not demos or decks; cuts scope intelligently to ship.
• Calm Technical Authority: Makes and defends decisions under ambiguity, and changes course quickly when evidence demands it.
• Force Multiplier: Gets more from the team than from their own keyboard - through mentoring, standards, and unblocking others.
• Governance as a Feature: Treats compliance, security, and auditability as design inputs that build trust, not friction to route around.
• Clear Communicator: Explains complex systems simply to executives and precisely to engineers; writes things down.
• Pragmatic Curiosity: Tracks the fast-moving AI landscape but adopts new tools only when they solve a real AJ Gallagher problem.