Senior Analyst - Gen AI Engineer

We are seeking an AI Engineer to design, build, and operationalize generative AI solutions for high-impact use cases across GBS functions. The role will partner with product managers, process owners, data engineers, privacy, security, and compliance teams to move from prototype to resilient and governed enterprise solutions, build AI workflows, and design / validate LLM-based extraction strategies using Microsoft low code/no code as well enterprise Azure tech stack. Responsibilities include developing and maintaining Python-based pipelines, crafting and evaluating prompts and orchestration logic, and ensuring solutions are reliable, repeatable, and production-ready. This role will blend applied AI engineering, product thinking, and disciplined execution, with a strong emphasis on communication, documentation, and quality assurance.

Responsibilities:

• Translate business needs into clear functional requirements and prompt design documentation, acceptance criteria, and test cases  
• Interact with business stakeholders to understand automation uses cases and triage based on ‘fit for purpose’ AI low code/no code as well as enterprise AI implementations  
• Write, version, and optimize prompts (system, user, tool-calls) to achieve required functionality across use cases  
• Conduct development in Microsoft Power Automate, Power Apps, Copilot Studio, Copilot Cowork, Azure AI Foundry, Azure Content Understanding, Azure App Service, Github Copilot, Python, SQL, Java, RAG pipelines, SODA framework  
• Design robust prompt patterns and guardrails for reliability, consistency, and compliance (e.g., role prompting, chain-of-thought redaction, constrained generation)  
• Diagnose error modes (hallucination, drift, formatting issues); implement mitigation strategies and continuous improvement cycles.  
• Maintain strong relationships and provide regular status updates; set expectations on AI capabilities, constraints, and risk  
• Create and maintain clear functional specifications, prompt playbooks, technical documentation, and runbooks  
• Prepare stakeholder-friendly summaries of findings, performance results, risks, and recommendations.  
• Contribute to standards for prompt governance, versioning, and reuse across the enterprise.  
• Adhere to data privacy, confidentiality and regulatory requirements relevant to insurance and risk management.  
• Apply responsible AI principles (bias awareness, explainability, auditability) and participate in model risk assessments.  
• Measure and improve quality using metrics such as precision/recall, accuracy, consistency, latency, and cost.  
• Develop evaluation plans and golden datasets; test AI functionality and validate outputs against requirements and source documents.  
• Ability to read/modify Python scripts, debug minor issues, and write clear, maintainable code. Familiarity with version control and basic CI/CD or repeatable execution (e.g., scripts, notebooks, pipelines). 

Competencies:

• Prompt Engineering & Generative AI: Expertise in Generative AI capabilities and tools; ability to create and test AI prompts to produce output based on requirements, implement LLM workflows (prompt engineering, RAG, agents, tools) using frameworks such as Semantic Kernel or LangChain, design and maintain vector stores, embeddings pipelines, and content moderation/guardrails to protect sensitive data (PII/PHI), evaluate models (quality, bias, latency, cost) and establish prompt/version governance and A/B testing.
• Data and MLOps: Build and operate data/feature pipelines and model services using Azure Databricks/ML, MLflow, and CI/CD (GitHub Actions/Azure DevOps), containerize and deploy on AKS or serverless where appropriate; implement monitoring for drift, accuracy, latency, and spend, collaborate with Data Engineering on data quality, lineage, cataloging, and secure access controls consistent with enterprise policy. 
• Business Acumen: Ability to understand Gallagher business objectives, processes, and systems; translate complex business problems into AI‑enabled solutions; and assess downstream impacts, risks, and value of AI use cases. 
• Data Literacy & Data Analysis: Ability to work with structured and unstructured data, including document analysis and information extraction. Familiarity with data profiling, data quality, metadata, and basic analytical techniques relevant to AI workflows. 
• Data Privacy, Security & Responsible AI: Knowledge of data privacy, security, and regulatory requirements (e.g., GDPR, CCPA). Knowledge of responsible AI, model risk, data management, and enterprise AI governance, embed privacy-by-design and model risk management practices aligned with regulatory requirements (e.g., GDPR, HIPAA where applicable), work with security, legal on Responsible AI guardrails, content filtering, red-teaming, and exception management.
• Stakeholder Communication and Interaction Skills: Ability to engage with business and technical stakeholders to gather requirements, produce technical documentation, explain AI outcomes, manage expectations, and resolve issues. Comfortable leading requirements discussions, reviews, and working sessions. 
• Problem-Solving Skills: Analysts need to be able to identify and define problems, as well as develop and implement effective solutions. They should be able to think critically and creatively to solve complex problems. 
• Agile delivery: Experience working in an Agile environment delivery, including user story grooming and sprint planning, development, and user acceptance. 
• Technical Skills: Analysts should have specific technical skills such as proficiency in M365 platform - Microsoft Power Automate, Power Apps, Copilot Studio, Copilot Cowork, Azure AI Foundry, Azure Content Understanding, Azure App Service, Github Copilot, Python, SQL, Java, RAG pipelines, SODA framework 
• Time Management: Analysts often work on multiple projects or tasks simultaneously. They should have strong time management skills to prioritize and meet deadlines. 
• Adaptability: Analysts should be adaptable and flexible to work in a fast-paced and changing environment. They should be able to quickly learn new tools, technologies, or methodologies as needed.  
• Attention to Detail: Ensure data quality and accuracy in all analyses and documentation. 

Qualifications:

• Minimum Required Degree: Bachelor's degree  
• Preferred Degree: An undergraduate degree in Computer Science, Data Science, Information Systems, or equivalent experience. 
• Certificate(s)/Special Training: Microsoft Certified: Azure AI Engineer Associate (AI‑102) preferred 

Experience:

• Mid-Level (1 to 3 Years) – Experience using Microsoft’s AI tech stack such a Copilot Studio, Power Apps and Power Automate.  
• Senior Level (3 to 6 Years) – hands on experience in AI/ML engineering, with at least 2 years building GenAI/LLM solutions in production. 

Knowledge, Skills and Ability:

• Experience engaging with business stakeholders to elicit requirements and translate them into AI‑enabled workflows, acceptance criteria, and success metrics, preferably within a Microsoft ecosystem. 

• Proven ability to design and optimize LLM prompts for document processing, data extraction, summarization, classification, and decision support use cases. 
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• Strong working knowledge of Microsoft Azure–based AI platforms and services, including: 

• Azure cloud services (Azure OpenAI Service, Azure AI Search - vector search and hybrid search, Azure Functions, Azure Storage, Azure AI Studio, Azure AI Foundry, Azure Content Understanding, Key Vault). 

• Experience building and orchestrating AI workflows using Azure‑aligned orchestration frameworks and tools, such as: 

• LangChain or LlamaIndex (in Azure‑hosted or Azure‑integrated deployments) 

• Azure AI Studio prompt flows or comparable orchestration capabilities 

• Hands-on experience with vector search and embedding strategies, preferably using: 
• Familiarity with alternatives (e.g., FAISS, Pinecone) as supplementary knowledge 

• Practical implementation experience with Microsoft Power Platform and developer tools, including: 

• Power Automate, Power Apps, and Copilot Studio 

• GitHub Copilot 

• Azure App Service or equivalent Azure hosting services 

• Strong programming and data skills, with hands-on experience in: 

• Python (primary) 

• SQL 

• Familiarity with Java where applicable 

• Designing and implementing RAG (Retrieval-Augmented Generation) pipelines 

• Applying structured evaluation or quality frameworks (e.g., SODA or similar) 

• Experience testing and validating AI solutions by: 

• Evaluating prompt outputs against source documents and business requirements 

• Applying a disciplined approach to iteration, traceability, and quality assurance 

• Strong written and verbal communication skills, including: 

• Leading requirements discussions, troubleshooting sessions, and status meetings with business and technical stakeholders 

• Producing clear functional and technical documentation, test plans, and user guidance 

• Practical understanding of advanced prompt engineering concepts, including: 

• Prompt design patterns 

• Context management and grounding 

• Few‑shot examples 

• Tool use / function calling 

• RAG‑based grounded generation 

• Ability to work independently as an individual contributor, manage multiple concurrent priorities, and deliver high‑quality outcomes within agreed timelines. 

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)