• Own the end-to-end technical vision for the entire AI/ML platform, from data ingestion, MLOps, model serving, fine-tuning, foundation model training, RAG, and agentic applications. • Make the critical "build vs. buy vs. open-source" decisions that balance speed, cost, and long-term defensibility. • Recruit, hire, mentor, and retain an elite team of T shaped AI engineers, applied ML engineers, data scientists, and platform engineers. • Design a rigorous hiring process to find "unicorn" talent and foster a culture of continuous learning and excellence. • Design, build, and scale an AI/ML platform that provides standardized tooling, infrastructure, and workflows for LLM training, fine-tuning, retrieval-augmented generation (RAG), AI orchestration, and deployment. • Develop reusable components and services (e.g., vector databases, prompt libraries, agent frameworks, model registries, evaluation pipelines, safety/guardrail modules) to accelerate delivery of AI solutions across product engineering teams. • Ensure reliability, scalability, and compliance of the AI/ML platform by implementing robust observability, governance, and cost-optimization strategies tailored for large model serving and API consumption. • Partner with business and product owners to identify, design, and implement high-impact AI solutions that drive measurable outcomes, ensuring alignment with strategic priorities. • Own the full lifecycle of AI solutions — from prototyping and deployment through ongoing monitoring, maintenance, and enhancements — ensuring solutions remain accurate, performant, and relevant as business needs evolve. • Continuously improve deployed AI systems by incorporating feedback, retraining models, and updating components to adapt to changing data, regulatory requirements, and operational realities. • Implement robust processes for quality assurance, model governance, and performance monitoring. • Drive adoption of a combination of hyper-scaler AI services and specialized cloud native AI solutions to accelerate time-to-market. • Lead cost observability and management initiatives to optimize AI infrastructure and usage at scale. • Ensure compliance with AI Governance policies. • Serve as the primary bridge between business stakeholders and the AI Office. • Ensure AI product roadmaps align with Natera’s broader AI vision and business objectives. • Champion a culture of responsible AI adoption, ensuring ethical, compliant, and explainable use of AI. • Translate business needs into actionable technical requirements for engineering and business teams. Collaborate with security, regulatory, compliance, and legal to ensure solutions meet security and regulatory standards. • Partner with AI Governance and Change Enablement leads to ensure trust, fairness, and adoption across the organization.