Day-to-day as a Technical Product Owner:- Lead cross-functional teams of machine learning scientists, machine learning engineers, data scientists and software engineers to deliver production-grade AI solutions.- Work closely with stakeholders to identify, refine and occasionally reject opportunities to build machine learning products; collaborate with support functions such as risk, technology, model risk management and incorporate interfacing features.- Translate business objectives into technically feasible AI/ML solutions and articulate trade-offs related to architecture, implementation complexity, scalability, risk, and model performance.- Develop the vision, strategy, and roadmap for AI technical products and capabilities that meet business objectives and maintain TD at the forefront of AI research and development.- Maintain an in-depth understanding of the solution, architecture, and technical implementation details to effectively challenge design decisions, assess delivery trade-offs, and prioritize development work in alignment with business and technical requirements.- Facilitate the professional and technical development of colleagues through mentorship and feedback.- Anticipate resource needs as solutions move through the model lifecycle, scaling pods up and down as models are built, perform, degrade and require enhancement.- Establish and uphold AI/ML engineering standards, best practices, and quality controls, while providing informed challenge on model design, MLOps, data architecture, and production-readiness decisions.- Lead the definition of cloud-native AI/ML solution architectures and operational strategies, partnering with engineering and architecture teams to ensure scalable deployment, monitoring, and lifecycle management of production AI systems.- Ensure AI solutions meet enterprise standards for security, governance, model risk management, responsible AI, compliance, operational resilience, and auditability.- Define, measure, and communicate the business value delivered by your products, connecting model performance to measurable business outcomes and enterprise AI value targets.