AI Developer

Gallagher•Rolling Meadows, IL

11d

About The Position

The AI Developer builds and ships the components that make Gallagher's enterprise AI solutions work. You will write production C#/.NET and Python code for RAG pipelines, AI agents, and Copilot integrations on our Azure stack, working from designs set by senior engineers while taking full ownership of the features assigned to you. This is a hands-on individual contributor role built for someone who learns fast, asks good questions, and turns what they learn into working software - in a regulated insurance environment where correctness, security, and auditability matter as much as speed.

Requirements

1-3 years of professional software engineering experience (internships and co-ops count), or a strong equivalent portfolio of shipped work.
Working proficiency in C#/.NET, Python, Typescript and Javascript, with the ability to become strong in .NET and Python; comfortable reading unfamiliar code.
Hands-on experience building or deploying on Azure (Functions, App Service, Container Apps, or equivalent), including authentication and configuration basics.
Practical experience calling LLM APIs and building something real with them - a RAG prototype, an agent, a chatbot, or an internal tool - whether at work, at school, or on your own.
Building and consuming REST APIs, working with JSON, and querying relational or vector data stores.
Git-based workflow, familiarity with CI/CD pipelines (Azure DevOps or equivalent), and a habit of testing your own work.
Bachelor's degree in Computer Science, Engineering, or a related technical field - or demonstrable equivalent experience.

Nice To Haves

Exposure to Azure infrastructure, Microsoft Foundry, Copilot Studio, Microsoft Agent Framework, or LangChain.
Any experience - internship, coursework, or prior role - in insurance, financial services, or healthcare, where compliance shapes how software is built.
Fluent use of AI coding assistants and agent workflows to move faster without sacrificing code quality.
Experience writing evals, test sets, or benchmarks for LLM outputs rather than judging quality by eye.
Reading or contributing to Terraform and similar infrastructure-as-code.
Azure certifications such as AZ-204, AI-102, or AI-900 are beneficial.

Responsibilities

Build Production AI Features: Implement well-scoped components of GenAI and agentic systems - retrieval pipelines, tool integrations, prompt flows, API endpoints - in C#/.NET and Python, following the team's established patterns.
Integrate Across the Azure Stack: Wire AI capabilities into enterprise services using Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure Functions, and Container Apps; connect to line-of-business systems through secure REST APIs and AI agents.
Ship to Enterprise Surfaces: Deliver working features into internal applications and support them once they are live.
Test and Evaluate Your Own Work: Write unit and integration tests, run the team's evaluation harnesses against your changes, and validate groundedness, accuracy, latency, and cost before asking for reviews.
Participate in Code and Design Reviews: Submit clean, reviewable pull requests, act on feedback quickly, and read others' code to learn the codebase and the standards behind it.
Follow Governance by Default: Apply the team's responsible-AI controls - Entra ID/RBAC, data-residency requirements, audit logging, human-in-the-loop checkpoints - as part of building the feature.
Debug and Support What You Build: Investigate production issues in AI pipelines and integrations, use logs and telemetry to isolate root causes, and deliver fixes with the same rigor as new features.
Learn Continuously and Apply It: Track how the AI tooling landscape is moving, run small experiments against real Gallagher problems, and bring what works back to the team with a clear explanation of why.
Communicate Clearly: Give honest status in stand-ups, flag blockers early, document what you built, and explain your work in terms teammates and business partners can follow.