Lead Instructor: MLOps / AI Platform Engineering

GA is at the leading edge of creating practical solutions to one of the most pressing challenges of our time - the future of work. As recognized by The World Economic Forum, BCG, the OECD and more, these are big challenges to which only a few companies are offering real solutions. In this role, you'll be speaking every day to corporate leaders who rely on GA to help them apply these solutions to their workforce of the future. 

Company: General Assembly

Client: Confidential – Customer Success Reskilling

Location: Remote (Must work West Coast / Pacific Time hours)

Duration: 2 Weeks (Starting Mid-June)

Commitment: Roughly 30 hours per week

Compensation: $15,500 flat fee for the project

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.