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

TD Bank’s Layer 6 AI research center is seeking a Technical Product Owner to lead production-grade delivery of AI, Machine Learning, and Generative AI solutions. This role combines technical depth with product ownership, cross-functional leadership, and governance-focused execution for cloud-native deployments.

Role Summary

The Technical Product Owner (Layer 6) drives the AI product vision and roadmap for TD’s research center, delivering solutions that are secure, governed, and production-ready. The position leads cross-functional technical delivery and ensures AI/ML systems are architected and operationalized to meet enterprise requirements, including responsible AI, compliance, model risk management, and auditability.

Responsibilities

• Lead cross-functional teams of machine learning scientists, machine learning engineers, data scientists, and software engineers to deliver production-grade AI solutions.
• Collaborate with stakeholders to identify, refine, and sometimes reject machine learning product opportunities; coordinate with support functions such as risk, technology, and model risk management while incorporating interfacing features.
• Translate business objectives into technically feasible AI/ML solutions, articulating trade-offs across 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 support TD’s research and development priorities.
• Maintain deep knowledge of solutions, architectures, and technical implementation details to challenge design decisions, assess delivery trade-offs, and prioritize development work aligned to business and technical requirements.
• Support the professional and technical development of colleagues through mentorship and feedback.
• Anticipate resource needs across the model lifecycle, scaling pods up and down as models are built, degrade, and require enhancement.
• Establish and enforce AI/ML engineering standards, best practices, and quality controls, including informed challenge on model design, MLOps, data architecture, and production readiness.
• Define cloud-native AI/ML solution architectures and operational strategies with engineering and architecture teams to enable scalable deployment, monitoring, and lifecycle management.
• Ensure AI solutions meet enterprise security, governance, model risk management, responsible AI, compliance, operational resilience, and auditability standards.
• Define, measure, and communicate business value by connecting model performance to measurable business outcomes and enterprise AI value targets.
• Use deep specialization expertise to ensure organization-wide teams that provision, manage, and support technologies have the necessary tools, processes, and documentation.
• Execute on engineering strategy for automation of build, test, release, and configuration across Application, Platform, and Infrastructure.
• Partner with Operations to integrate automated and self-serve releases through appropriate tools and processes.
• Work with technology partners to identify and leverage synergies based on relevant business needs.
• Act as expert or lead innovator and change agent for managed programs and services.
• Stay current on trends, products, frameworks, and applications with vendor platform providers and engineering peers.
• Manage stakeholder engagement and enterprise impacts across the organization.
• Adhere to enterprise frameworks and methodologies relevant to the business area.
• Escalate issues when appropriate and consistently handle confidentiality, correspondence, and information with discretion.
• Ensure business operations comply with applicable internal and external requirements, including financial controls, segregation of duties, transaction approvals, and physical control of assets.
• Participate in cross-functional initiatives as a subject matter expert to identify risks and provide guidance for complex situations.
• Conduct internal and external research projects and support presentations and communications to management or broader audiences.
• Perform meaningful analysis at functional or enterprise levels to draw conclusions, make recommendations, and assess program effectiveness.
• Monitor service and productivity and implement continuous process or performance improvements where opportunities exist.
• Lead and implement remediation plans to address performance, risk, and governance issues.
• Keep abreast of emerging issues, trends, and evolving regulatory requirements and assess potential impacts.
• Maintain a culture of risk management and control aligned with risk appetite.
• Contribute to a positive work environment through timely communication and support for quality, innovation, and teamwork.
• Provide thought leadership and participate in knowledge transfer within the team and business unit, while keeping current on emerging trends and developments.
• Participate in personal performance management and development activities, including cross training within the team.
• Maintain regular project and day-to-day status communication with internal partners as needed.
• Contribute to team skill development through mentorship and sharing knowledge and best practices.
• Lead, motivate, and develop relationships with internal and external stakeholders to build productive working relationships.
• Support a fair, positive, and equitable environment that supports a diverse workforce.
• Serve as an ambassador for ML Engineering and for the bank, internally and/or externally.

Requirements

• 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.
• Undergraduate degree required.
• Advanced degree in relevant technical fields (Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, Mathematics, or related) or equivalent practical experience delivering complex AI/ML solutions.

Technologies

• machine learning, deep learning, generative AI
• time series forecasting
• large language models, retrieval systems
• Python, SQL
• CI/CD, version control, automated testing, observability, deployment automation
• MLOps, cloud-native
• security, governance, model risk management, responsible AI, compliance
• operational resilience, auditability
• data engineering

Work Location and Schedule

• Location: New York, New York, United States (onsite)
• Hours: 40

Compensation

• Base salary range: USD 96,130 to 181,944 per year
• Pay details include eligibility for base salary and variable compensation/incentive awards, such as cash and/or equity incentive awards

Benefits

• Health and well-being benefits
• Savings and retirement programs
• Paid time off (Vacation PTO, Flex PTO, and Holiday PTO)
• Banking benefits and discounts
• Career development
• Access to an online learning platform and mentoring programs
• Training and onboarding sessions

Additional Information

• Interview process: candidates of interest will be reached out to schedule an interview; outcomes communicated by email or phone call
• Accommodation email: [email protected]
• Regular development conversations and training programs, as well as a competitive benefits plan

Physical Requirements

• Domestic travel: occasional
• International travel: never
• Sedentary work: continuous
• Multiple tasks: continuous
• Operating standard office equipment: continuous
• Responding quickly to sounds: occasional
• Sitting: continuous
• Standing: occasional
• Walking: occasional
• Other movements and lifting: occasional lifting/carrying under 25 lbs.; lifting/carrying over 25 lbs.: never
• Remaining listed physical activities are defined as continuous, occasional, or never as specified in the posting

Department Overview

Layer 6 is TD Bank Group’s AI research center of excellence. The team develops and deploys machine learning systems impacting the lives of over 27 million customers, working across deep learning and generative AI, time series forecasting, and responsible use of AI. The group has access to large financial datasets and actively collaborates with world-renowned academic faculty.