About this role

Review and validate competencies and learning objectives for the MLOps / AI Platform Engineer pathway
Validate technical accuracy of instructional slide content covering ML pipelines, model deployment, monitoring, and governance
Review async assets including prompt-alongs and self-paced exercises for technical correctness and appropriate difficulty level for the learner population (experienced customer-facing professionals, not engineers)
Participate in one structured SME review gate (approximately 1 week, early June)
Provide a single round of revision feedback for the LED to implement before QA

7+ years in software or data engineering with 3+ years in MLOps or ML platform roles in production
Hands-on experience with ML pipelines, model deployment, monitoring, and governance at scale
Strong DevOps and CI/CD fundamentals applied to ML workloads
Python proficiency, data engineering foundations, and Azure cloud infrastructure fluency
Familiarity with Azure ML, AI Foundry platform engineering patterns, and model lifecycle management
AZ-900, AI-900, and DP-100 minimum; AI-102 preferred
Former AI Platform or Azure ML engineer with Microsoft, Google, or similar company is a strong plus

MLOps Engineer, AI Platform Expert

Key skills