Technical Product Owner (Layer 6) - AI & Machine Learning Solutions

Work Location: New York, New York, United States of America Hours: 40 Pay Details: $96,130 - $181,944 USD TD is committed to providing fair and equitable compensation opportunities to all colleagues. Growth opportunities and skill development are defining features of the colleague experience at TD. Our compensation policies and practices have been designed to allow colleagues to progress through the salary range over time as they progress in their role. The base pay actually offered may vary based upon the candidate's skills and experience, job-related knowledge, geographic location, and other specific business and organizational needs. As a candidate, you are encouraged to ask compensation related questions and have an open dialogue with your recruiter who can provide you more specific details for this role. Line of Business: Analytics, Insights, & Artificial Intelligence Job Description: Department Overview: Layer 6 is the AI research center of excellence for TD Bank Group. We develop and deploy industry-leading machine learning systems that impact the lives of over 27 million customers, helping more people achieve their financial goals and needs. Our research broadly spans the field of machine learning with areas such as deep learning and generative AI, time series forecasting and responsible use of AI. We have access to massive financial datasets and actively collaborate with world renowned academic faculty. We are looking for people driven to be at the cutting edge of machine learning in research, engineering, and impactful applications. Position Overview: This is a highly technical role focused on the delivery of AI, Machine Learning, and Generative AI solutions. Successful candidates will have hands-on experience working with machine learning engineers, data scientists, software engineers, and cloud/platform teams to deliver production AI systems. This role requires the ability to evaluate technical designs, challenge architectural decisions, and drive technical delivery outcomes. Day-to-day as a Technical Product Owner (Machine Learning Engineer II): 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. Preferred Qualifications: 5+ years of experience delivering complex AI, machine learning, data science, advanced analytics, or software products in large-scale enterprise environments. 3+ years of experience leading cross-functional technical delivery teams consisting of machine learning engineers, data scientists, software engineers, and MLOps practitioners. Deep understanding of machine learning, generative AI, large language models, retrieval systems, model evaluation techniques, data engineering, MLOps, and software development practices. Ability to evaluate technical designs and challenge architecture decisions while partnering effectively with engineering and research teams. Strong technical proficiency in Python, SQL, and modern software engineering practices including version control, CI/CD, automated testing, observability, and deployment automation. Familiarity with model risk management and AI governance in regulated industries. Advanced degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, Mathematics, or a related technical field; or equivalent practical experience delivering complex AI/ML solutions. Depth & Scope: Expert knowledge of specific domain or range of engineering frameworks, technology, tools, processes, and procedures, as well as organization issues Expert knowledge of TD applications, systems, networks, innovation, design activities, best practices, business / organization, Bank standards, and may fulfill a governance role Integrates knowledge of business and functional priorities and acts as a key contributor in a complex and critical environment Adept at technical project execution, strategic planning, and effective communication. Accountable for specialized knowledge in a field of AI/ML Engineering and may provide leadership to teams or projects, shares expertise Applies in-depth skills and broad knowledge of the business to address complex problems and nonstandard situations Education & Experience: Undergraduate degree required, advanced technical degree preferred (e.g., math, physics, engineering, finance or computer science) Graduate's degree preferred with either progressive project work experience or 1 +years relevant experience (includes post graduate experience) Customer Accountabilities: Leverages deep technology expertise for own area of specialization to deliver and ensure that all areas across the organization that provision, manage, and support various technologies have the necessary tools, processes and documentation required to effectively execute on their respective mandates Executes on Engineering strategy as it relates to the introduction of tools and the automation of build, test, release and configure activities across Application, Platform and Infrastructure. Partners with the Operations team to automatically integrate with appropriate tools and processes as part of automated/self-serve Application, Platform, or Infrastructure releases Works with partners across Technology and apply in depth understanding of relevant business needs to identify and leverage synergies across the various areas Acts as the expert or lead innovator and agent of change for the programs and services under management. Works with other teams to implement best practices for engineering and management and serve as an ambassador for ML Engineering Works with vendor platform providers and engineering peers to keep abreast of trends, products, frameworks, and applications Identifies and effectively manages stakeholder engagement and impacts across the enterprise Interprets client needs, assess engineering related requirements, and identify solutions to non-standard requests Shareholder Accountabilities: Adheres to enterprise frameworks or methodologies that relate to activi