This is a highly technical role focused on the delivery of AI, Machine Learning, and Generative AI solutions. Own the delivery of ML solutions by spearheading a team of scientists and engineers. Meld your expertise in data science, technology, and product management to solve strategic business problems with innovative, scalable products. Evaluate technical designs, challenge architectural decisions, and drive technical delivery outcomes. Show your passion for building a better solution, every time. Provide your colleagues with the inspiration and tools to execute with speed and impact, becoming true leaders themselves.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.