MLOps / AI Platform Engineer Subject Matter Expert

Accountabilities

In this role, you will act as the primary technical authority ensuring the accuracy and industry relevance of an MLOps / AI Platform Engineering learning pathway. You will focus on validating content, providing structured feedback, and aligning learning outcomes with real-world engineering standards:

• Review and validate competencies, learning objectives, and technical curriculum for an MLOps and AI Platform Engineering pathway.

• Assess instructional materials covering ML pipelines, model deployment, monitoring, and governance to ensure technical correctness and industry alignment.

• Evaluate asynchronous learning assets such as exercises and guided activities for accuracy, complexity, and suitability for transitioning professionals.

• Participate in a structured SME review cycle and provide consolidated, actionable feedback for implementation by instructional designers.

• Ensure content reflects real-world production practices in ML systems, including scalability, reliability, and operational governance.

Requirements

The ideal candidate brings deep production experience in machine learning systems and strong exposure to MLOps and platform engineering practices, combined with the ability to evaluate and guide educational content:

• 7+ years of experience in software or data engineering, with at least 3+ years in MLOps or ML platform engineering roles in production environments.

• Strong hands-on expertise with ML pipelines, model deployment, monitoring, and governance at scale.

• Solid understanding of DevOps principles, CI/CD workflows, and their application to machine learning systems.

• Proficiency in Python, data engineering fundamentals, and cloud platforms (preferably Azure).

• Experience with Azure ML, AI platform engineering patterns, and full model lifecycle management.

• Familiarity with certifications such as AZ-900, AI-900, DP-100 (AI-102 preferred).

• Strong communication skills with the ability to translate complex technical feedback into clear, actionable guidance for non-engineering audiences.

• Experience in leading-edge AI platform environments (e.g., Microsoft, Google, or similar) is a strong advantage.

Benefits

• Competitive hourly compensation ranging from $60 to $75 per hour

• Flexible, remote, part-time engagement (15–20 hours per week)

• Short-term project commitment (6–7 weeks) with defined scope and deliverables

• Opportunity to shape an industry-relevant AI/MLOps reskilling program

• Fully asynchronous collaboration with limited live meeting requirements

• Exposure to innovative workforce transformation initiatives in AI and machine learning education

• Ability to influence how real-world AI platform engineering is taught to transitioning professionals

How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
 Why Apply Through Jobgether?

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