Position Overview
The Subject Matter Expert (SME) will lead the technical development and validation of Generative AI Operations for Healthcare with Azure. This role is critical for ensuring that advanced AI concepts—specifically Large Language Models (LLMs) and Generative AI—are applied accurately within clinical and administrative healthcare workflows using the Azure AI ecosystem.
Role Summary
The SME acts as the primary technical authority, bridging the gap between raw AI capabilities and healthcare-specific applications. You will be responsible for the accuracy of the Program Architecture, ensuring that all instructional materials, code samples, and labs reflect current industry best practices and Microsoft's technical standards.
Responsibilities
Discovery & Validation
- Validate and refine the JTA to ensure the curriculum aligns with the real-world tasks of a Healthcare AI Operations Engineer.
- Assess and validate the relevance of Coursera's In-Demand Topic (IDT) framework to ensure focus on essential areas such as generative AI deployment, LLMOps, prompt engineering, and healthcare AI governance
Content Development & AI Validation
- Healthcare GenAI Technical Content Areas: Large Language Models (LLMs): Fine-tuning and prompt engineering for clinical documentation.; RAG (Retrieval-Augmented Generation): Implementing Azure AI Search for medical knowledge bases.; Responsible AI: Validating frameworks for bias mitigation and HIPAA-compliant AI operations.
- Develop instructional materials, including case studies, concept guides, and interactive scripts that cover the key topics
- Program Architecture: Oversee the architectural integrity of the course, ensuring that Add-on modules and core content integrate seamlessly.
Atlas (Course Blueprint/Storyboard) Commentary & Improvement Recommendations
- Provide specific, actionable improvement recommendations in Atlas comments—ensuring technical flow and accuracy of the narrative
Multimedia & Instructional Production
- Support the creation of multimedia content, including instructional videos and slide decks that demonstrate practical applications of generative AI operations in healthcare
- Develop datasets and templates for simulated healthcare GenAI projects
- Create architecture diagrams for healthcare GenAI systems
- Develop sample deployment pipelines for healthcare LLMOps
- Review scripts and visual assets to ensure technical demonstrations of Azure tools are accurate and up-to-date.
Assessment Design & Validation
- Create formative and summative assessments, including quizzes, hands-on tasks, and real-world healthcare case scenarios to evaluate learner comprehension and application of concepts
- Labs: Design and validate hands-on Labs that simulate real-world Azure environments (e.g., deploying an Azure OpenAI endpoint).
- Capstone Project: Develop a comprehensive Capstone project and associated rubric where learners must build a generative AI solution for a healthcare use case (e.g., an automated patient query system).
- Summative Assessment: Create rigorous final exams and corresponding rubric that test both theoretical knowledge and practical application.
Required Qualifications
Education
- Master's degree in Computer Science, Data Science, Health Informatics, Information Technology, or related field (12+ years directly relevant experience may be considered)
Experience
- 5-10 years in AI/ML engineering or operations (3+ years focused on generative AI technologies and LLM deployments)
Clinical Domain Knowledge
- Proven experience navigating healthcare data standards (FHIR, HL7) and regulatory requirements (HIPAA, GDPR).
Microsoft Product Mastery
- Azure OpenAI Service: Deep expertise in deploying GPT models and managing tokens/rate limits.
- Azure AI Search: Experience building vector databases for RAG.
- Azure Machine Learning: Proficiency in prompt flow and LLMOps.
Certifications (Preferred)
- Microsoft Certified: Azure AI Engineer Associate (AI-102) – Required
- Microsoft Certified: Azure Solutions Architect Expert – Required
Capabilities
- Technical Skills: Advanced Python programming, PyTorch/TensorFlow, and API integration.
- Collaboration: Ability to provide constructive feedback to instructional designers and production teams under tight deadlines.
- Time Commitment: Availability for 15-20 hours per week during peak development phases.
