Lead AI Engineer (GenAI / Applied AI)
About The Position
Peter Millar is seeking a Lead AI Engineer to provide dedicated leadership for AI engineering, bridging data engineering, data science, and business applications. This role is responsible for the entire lifecycle of AI solutions, including model selection, LLM/RAG integration, deployment, monitoring, and governance. The Lead AI Engineer will operationalize AI safely and at scale on Microsoft Foundry (formerly Azure AI Foundry) and the Azure AI stack, utilizing Peter Millar’s governed Microsoft Fabric and OneLake data. The position involves establishing a repeatable framework for AI use cases, implementing MLOps discipline, and eventually building and managing a team of AI/ML engineers.
Requirements
- Deep, hands-on experience with Microsoft Foundry (Azure AI Foundry), including the model catalog, Foundry Agent Service, Foundry Tools, evaluation/observability, and deployment.
- Production experience with Azure AI services and Azure OpenAI.
- Experience with LLM/RAG architectures and prompt engineering.
- Experience grounding AI on governed Microsoft Fabric / OneLake data (Lakehouse, Direct Lake, shortcuts).
- Strong MLOps skills including CI/CD, monitoring, evaluation, and cost management using Python.
- Experience implementing AI governance, including PII handling, prompt security, and output validation.
- 8+ years in ML/AI engineering.
- 2+ years leading or managing engineers (or strong technical-lead experience).
- Proven delivery of production generative-AI solutions (RAG, copilots, agents) at enterprise scale.
- Bachelor’s or Master’s in Computer Science, Machine Learning, or a related field.
Nice To Haves
- Hands-on Microsoft Foundry / Azure AI Foundry experience strongly preferred.
- Familiarity with Microsoft Fabric / OneLake and governed data foundations.
- Relevant Azure AI certifications are a plus.
Responsibilities
- Own the AI platform built on Microsoft Foundry (Azure AI Foundry), including model catalog selection, prompt configuration, evaluation, and deployment.
- Set up the Foundry Agent Service for production agents, covering conversation management, tool calling, identity, safety, and observability.
- Build RAG pipelines grounded in governed OneLake data to support copilots, search, and personalization.
- Reduce reliance on consultants and avoid fragmented point solutions.
- Introduce monitoring, evaluation, retraining, and cost-management practices to prevent model degradation.
- 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.
- Implement guardrails for 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.
- Act as hiring manager and technical lead for future AI/ML engineers, defining specialization pathways.
- Translate high-value business problems into production-ready AI solutions with measurable ROI.
- Maximize ROI on existing Fabric, semantic-layer, and MDM investments.
Benefits
- Equal opportunity employer
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What This Job Offers
Job Type
Full-time
Career Level
Senior
