• Architect and spearhead the development of cutting-edge, scalable AI infrastructure, including novel human-in-the-loop (HITL) paradigms, ensuring our systems learn effectively from feedback. • Lead the technical design and implementation of core MLOps components and systems for our LLMs—including CI/CD, monitoring, and automated feedback loops—ensuring robustness, scalability, and adherence to software engineering best practices. • Define and shape solutions for complex automation and deployment challenges, enabling the strategic application of our cutting-edge AI. • Drive technical alignment and integration with AI Data Science and Software Engineering teams, ensuring the seamless transition of AI solutions from research into production environments and influencing architectural standards. • Define and establish standards for the rigorous validation, monitoring, and lifecycle management of AI products, ensuring continuous accuracy improvement and reliability in production. • Define, champion, and drive adoption of best practices for MLOps, including model/data versioning, experiment tracking, and reproducibility within the AI/ML domain; actively mentor others. • Identify, champion, and integrate state-of-the-art MLOps technologies and frameworks, driving innovation and maintaining our technical edge in AI deployment. • Provide expert guidance on applying safeguards and protections (HIPAA, privacy laws) to our model deployment and data handling pipelines; champion and uphold the highest compliance, quality, and security standards.