Lead AI Engineer (GenAI / Applied AI)

Essential Functions

• AI Platform Ownership (Pilot → Production)
• 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.
• Stand up the Foundry Agent Service for production agents, conversation management, tool calling, identity, safety, and observability.
• Build RAG pipelines grounded in governed OneLake data; establish a repeatable framework for copilots, search, and personalization.
• Reduce dependency on consultants and avoid fragmented point solutions.

MLOps & Lifecycle Discipline

• Implement monitoring, evaluation, retraining, and cost-management practices to prevent model degradation over time.
• Implement CI/CD for AI artifacts, versioning of prompts and models, and automated evaluation.
• Partner with data engineering to ensure Microsoft Fabric / OneLake data is AI-ready.

Governance & Risk Mitigation

• Implement guardrails: PII handling, model evaluation, prompt-injection defense, output validation, and content safety.
• Ensure alignment with enterprise and Richemont AI policies and compliance requirements.
• Establish responsible-AI standards and documentation.

Team Leadership & Delivery

• Act as hiring manager and technical lead for future AI/ML engineers; define the specialization pathway.
• Translate high-value business problems into production-ready AI solutions with measurable ROI.
• Maximize ROI on existing Fabric, semantic-layer, and MDM investments — avoiding “data rich, AI poor” outcomes.

Technical Competencies (required)

• Microsoft Foundry (Azure AI Foundry) — deep, hands-on experience with the model catalog, Foundry Agent Service, Foundry Tools, evaluation/observability, and deployment.
• Azure AI — production experience with Azure AI services and Azure OpenAI, LLM/RAG architectures, and prompt engineering.
• Microsoft Fabric & OneLake — experience grounding AI on governed Fabric/OneLake data (Lakehouse, Direct Lake, shortcuts).
• MLOps — strong CI/CD, monitoring, evaluation, and cost management; Python.
• AI governance — experience implementing PII handling, prompt security, and output validation.

Desired Education And Experience

• 8+ years in ML/AI engineering, including 2+ years leading or managing engineers (or strong technical-lead experience).
• Proven delivery of production generative-AI solutions (RAG, copilots, agents) at enterprise scale.
• Hands-on Microsoft Foundry / Azure AI Foundry experience strongly preferred.
• Familiarity with Microsoft Fabric / OneLake and governed data foundations.
• Bachelor’s or Master’s in Computer Science, Machine Learning, or a related field; relevant Azure AI certifications a plus.