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

TD • New York, NY

1d • $96,130 - $181,944

About The Position

The Technical Product Owner will lead the delivery of AI, machine learning, and generative AI solutions at Layer 6, TD Bank Group’s AI research center of excellence. The role works with machine learning scientists and engineers, data scientists, software engineers, cloud and platform teams, and business stakeholders to deliver production-grade AI systems. It requires hands-on technical understanding and the ability to assess designs, challenge architectural decisions, and guide technical delivery.

The position develops product vision, strategy, and roadmaps; translates business objectives into technically feasible solutions; and weighs architecture, complexity, scalability, risk, and model performance. The Technical Product Owner maintains detailed knowledge of solution architecture and implementation, supports engineering standards and quality controls, and ensures production systems meet enterprise expectations for security, governance, responsible AI, model risk management, compliance, resilience, and auditability. The role also measures and communicates product value, plans resources across the model lifecycle, and supports colleague development through mentorship and feedback.

Requirements

• 5+ years of experience delivering complex AI, machine learning, data science, advanced analytics, or software products in large-scale enterprise environments (listed as a preferred qualification).
• 3+ years of experience leading cross-functional technical delivery teams including machine learning engineers, data scientists, software engineers, and MLOps practitioners (listed as a preferred qualification).
• Deep understanding of machine learning, generative AI, large language models, retrieval systems, model evaluation, 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.
• Undergraduate degree required; an advanced technical degree is preferred. The posting also describes an advanced degree or equivalent practical experience delivering complex AI/ML solutions as a preferred qualification.
• Relevant experience is specified in the Education & Experience section as “1 +years relevant experience” for graduates with progressive project work experience or a graduate degree; the posting does not clarify this wording.
• Expert knowledge of a relevant domain or range of engineering frameworks, technologies, tools, processes, procedures, and organizational issues.
• In-depth skills and broad business knowledge to address complex problems and nonstandard situations; capability in technical project execution, strategic planning, and effective communication.

Nice To Haves

• 3+ years leading cross-functional technical delivery teams including machine learning engineers, data scientists, software engineers, and MLOps practitioners.
• 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.
• Graduate degree and progressive project work experience.

Responsibilities

• Lead cross-functional teams of machine learning scientists, machine learning engineers, data scientists, and software engineers to deliver production-grade AI solutions.
• Work with stakeholders to identify, refine, and sometimes reject opportunities for machine learning products, collaborating with risk, technology, model risk management, and other support functions.
• Translate business objectives into technically feasible AI/ML solutions and communicate trade-offs involving architecture, implementation complexity, scalability, risk, and model performance.
• Develop vision, strategy, and roadmaps for AI technical products and capabilities aligned with business objectives and AI research and development.
• Maintain in-depth understanding of solution architecture and technical implementation to challenge design decisions, assess delivery trade-offs, and prioritize development work.
• Support colleagues’ professional and technical development through mentorship and feedback.
• Anticipate resource needs throughout the model lifecycle and scale teams as models are built, perform, degrade, or require enhancement.
• Establish and uphold AI/ML engineering standards, best practices, and quality controls; provide informed challenge on model design, MLOps, data architecture, and production readiness.
• Lead definition of cloud-native AI/ML solution architectures and operational strategies with engineering and architecture teams, supporting scalable deployment, monitoring, and lifecycle management.
• Ensure AI solutions meet enterprise standards for security, governance, model risk management, responsible AI, compliance, operational resilience, and auditability.
• Define, measure, and communicate product business value, connecting model performance to business outcomes and enterprise AI value targets.
• Leverage technology expertise to ensure technology teams have the tools, processes, and documentation needed to execute their mandates.
• Execute engineering strategy for tools and automation of build, test, release, and configuration activities across application, platform, and infrastructure; partner with Operations on automated or self-service releases.
• Partner across Technology to understand business needs and identify synergies; act as an expert, innovator, and change agent for managed programs and services.
• Promote engineering and management best practices and serve as an ambassador for ML Engineering.
• Work with vendor platform providers and engineering peers to stay informed about trends, products, frameworks, and applications.
• Manage stakeholder engagement and impacts across the enterprise; interpret client needs and identify solutions to non-standard engineering requests.
• Adhere to applicable enterprise frameworks, policies, controls, and regulatory requirements; manage confidentiality and escalate issues appropriately.
• Participate as a subject matter expert in cross-functional and enterprise initiatives, conduct research and analysis, make recommendations, and support presentations and communications.
• Monitor service, productivity, and efficiency; implement process improvements and lead or facilitate remediation of performance, risk, or governance issues.
• Maintain relationships across business lines and control functions, keep abreast of emerging issues and regulations, and support a culture of risk management and control.
• Contribute to a positive, inclusive team environment; share expertise, keep colleagues informed, develop team capabilities, build productive stakeholder relationships, and represent the business area and bank.

Benefits

• Health and well-being benefits
• Savings and retirement programs
• Paid time off, including Vacation PTO, Flex PTO, and Holiday PTO
• Banking benefits and discounts
• Career development
• Reward and recognition
• Variable compensation and incentive awards, potentially including cash and/or equity incentive awards
• Training programs and onboarding
• Online learning platform and mentoring programs

What This Job Offers

• Job Type: Full-time
• Career Level: Senior