The Lead AI Engineer provides dedicated leadership for AI engineering, sitting between data engineering, data science, and business applications. The role owns the end-to-end lifecycle of AI solutions; from model selection and LLM/RAG integration to deployment, monitoring, and governance. They be responsible for operationalizing AI safely at scale on Microsoft Foundry (formerly Azure AI Foundry) and the broader Azure AI stack, grounded in Peter Millar's governed Microsoft Fabric and OneLake data. This person establishes a repeatable framework for delivering AI use cases, introduces MLOps discipline, and over time builds and manages a small team of AI/ML engineers ESSENTIAL FUNCTIONS: AI Platform Ownership (Pilot 14 Production) 02 Own the AI platform built on Microsoft Foundry (Azure AI Foundry): model-catalog selection (OpenAI, Anthropic, Meta, open and frontier models), prompt configuration, evaluation, and deployment. 02 Stand up the Foundry Agent Service for production agents, conversation management, tool calling, identity, safety, and observability. 02 Build RAG pipelines grounded in governed OneLake data; establish a repeatable framework for copilots, search, and personalization. 02 Reduce dependency on consultants and avoid fragmented point solutions. MLOps & Lifecycle Discipline 02 Introduce monitoring, evaluation, retraining, and cost-management practices to prevent model degradation over time. 02 Implement CI/CD for AI artifacts, versioning of prompts and models, and automated evaluation. 02 Partner with data engineering to ensure Microsoft Fabric / OneLake data is AI-ready. Governance & Risk Mitigation 02 Implement guardrails: PII handling, model evaluation, prompt-injection defense, output validation, and content safety. 02 Ensure alignment with enterprise and Richemont AI policies and compliance requirements. 02 Establish responsible-AI standards and documentation. Team Leadership & Delivery 02 Act as hiring manager and technical lead for future AI/ML engineers; define the specialization pathway. 02 Translate high-value business problems into production-ready AI solutions with measurable ROI. 02 Maximize ROI on existing Fabric, semantic-layer, and MDM investments - avoiding "data rich, AI poor" outcomes. TECHNICAL COMPETENCIES (REQUIRED): 02 Microsoft Foundry (Azure AI Foundry) - deep, hands-on experience with the model catalog, Foundry Agent Service, Foundry Tools, evaluation/observability, and deployment. 02 Azure AI - production experience with Azure AI services and Azure OpenAI, LLM/RAG architectures, and prompt engineering. 02 Microsoft Fabric & OneLake - experience grounding AI on governed Fabric/OneLake data (Lakehouse, Direct Lake, shortcuts). 02 MLOps - strong CI/CD, monitoring, evaluation, and cost management; Python. 02 AI governance - experience implementing PII handling, prompt security, and output validation. DESIRED EDUCATION AND EXPERIENCE: 02 8+ years in ML/AI engineering, including 2+ years leading or managing engineers (or strong technical-lead experience). 02 Proven delivery of production generative-AI solutions (RAG, copilots, agents) at enterprise scale. 02 Hands-on Microsoft Foundry / Azure AI Foundry experience strongly preferred. 02 Familiarity with Microsoft Fabric / OneLake and governed data foundations. 02 Bachelor's or Master's in Computer Science, Machine Learning, or a related field; relevant Azure AI certifications a plus.