General Assembly is delivering a specialized reskilling program designed to transition Customer Success and Account Management professionals into MLOps and AI Platform Engineering roles.
As the Lead Instructor , you will be the face of lessons. You aren't just checking the math; you are bridge-building. You will lead experienced customer-facing professionals through the complexities of taking AI systems from pilot to production, ensuring they leave the 2-week intensive with a functional understanding of MLOps governance and deployment.
- Lead Live Instruction: Deliver high-energy, synchronous remote lectures and "prompt-along" sessions covering ML pipelines, model deployment, and monitoring.
- Simplify the Complex: Translate high-level MLOps concepts (CI/CD for ML, governance frameworks) into digestible insights for learners who are experienced professionals but not career engineers.
- Facilitate Hands-on Labs: Guide students through self-paced exercises and live troubleshooting within the Azure AI Foundry and ML environments.
- Mentor & Office Hours: Provide real-time feedback during dedicated lab hours, helping students navigate technical roadblocks in Python and Azure infrastructure.
- Drive Learning Outcomes: Ensure students can successfully articulate and execute model lifecycle management strategies by the end of the cohort.
- The Experience: 7+ years in software or data engineering, with at least 3+ years specifically in MLOps or ML platform roles in a production environment.
- The "Teacher" Gene: Proven experience in technical instruction, bootcamp delivery, or corporate training. You should be comfortable "reading the room" in a virtual setting.
- Azure Fluency: Deep, hands-on expertise with Azure ML and AI Foundry . You should be able to navigate these platforms in your sleep.
- Technical Foundation: Proficiency in Python, Data Engineering fundamentals, and applying DevOps/CI/CD principles specifically to ML workloads.
- The Credentials: AZ-900, AI-900, and DP-100 are required; AI-102 is preferred.
- The Pedigree: Experience as an AI Platform or Azure ML engineer at a major tech firm (Microsoft, Google, etc.) is a massive plus.
Note on Schedule: This is a high-intensity, 2-week engagement. Candidates must be fully available for 30 hours per week during the mid-June window and be prepared to operate on Pacific Time (PT) schedules to align with the learner cohort.