AI Engineer

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 LLM Engineer designs, builds, deploys, and optimizes large language model solutions for enterprise client use cases. This role combines machine learning, software engineering, and applied AI expertise to create production-ready systems such as copilots, document intelligence solutions, agentic workflows, and retrieval-augmented generation applications. The ideal candidate can translate business needs into scalable, secure, and reliable LLM-powered products while maintaining a strong focus on evaluation, safety, performance, and cost efficiency. How You'll Make An Impact • Design and develop LLM-powered applications, including chat assistants, knowledge retrieval solutions, summarization tools, and agent-based workflows. • Build and maintain retrieval-augmented generation pipelines using enterprise content sources, vector databases, embeddings, reranking, and prompt orchestration. • Fine-tune, evaluate, and optimize open-source and hosted foundation models for domain-specific use cases. • Create prompt engineering frameworks, reusable templates, and testing methods to improve answer quality, consistency, and reliability. • Implement evaluation pipelines to measure accuracy, grounding, latency, safety, hallucination rates, and user experience. • Develop APIs, microservices, and scalable backend services that integrate LLM capabilities into business applications. • Partner with product managers, designers, data engineers, security, and compliance teams to deliver enterprise-ready AI solutions. • Establish observability, monitoring, and incident response practices for production AI systems. • Improve model and system efficiency through caching, batching, routing, quantization, and inference optimization. • Support governance requirements including data privacy, model risk management, security controls, and responsible AI standards. About You Required Qualifications • Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field; Master's degree preferred. • 3+ years of experience in software engineering, machine learning engineering, applied AI, or natural language processing. • Hands-on experience building solutions with large language models, foundation models, or generative AI platforms. • Strong proficiency in Python and experience with modern software engineering practices, including testing, version control, and CI/CD. • Experience with LLM frameworks and tooling such as LangChain, LlamaIndex, Semantic Kernel, or equivalent orchestration frameworks. • Knowledge of embeddings, vector databases, semantic search, retrieval systems, and prompt engineering techniques. • Experience deploying AI services in cloud environments such as Azure, AWS, or Google Cloud. • Understanding of model evaluation, experimentation, and performance optimization in production systems. • Familiarity with responsible AI practices, including safety, privacy, bias mitigation, and governance controls. • Strong communication skills with the ability to explain technical concepts to non-technical stakeholders. Preferred Qualifications • Experience fine-tuning models using techniques such as LoRA, QLoRA, or parameter-efficient tuning. • Experience with open-source models and model serving frameworks. • Knowledge of distributed inference, GPU optimization, and containerized deployment with Kubernetes or similar platforms. • Experience building enterprise copilots, internal knowledge assistants, or AI automation tools. • Familiarity with evaluation frameworks, human-in-the-loop review processes, and red teaming practices. • Knowledge of secure enterprise architecture patterns for integrating AI with internal data sources and applications. • Experience working in regulated industries such as insurance, healthcare, or financial services. Core Skills Large language models, generative AI, retrieval-augmented generation, prompt engineering, vector databases, model evaluation, API development, Python, cloud engineering, MLOps, data pipelines, observability, responsible AI, security, and cross-functional collaboration. What Success Looks L