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

Layer 6 is TD Bank’s AI research center of excellence, developing and deploying machine learning systems that support millions of customers. In this highly technical Technical Product Owner role, you will help translate AI research into production-grade AI, Machine Learning, and Generative AI solutions, with a focus on secure, governed, and production-ready deployment. The work is grounded in deep technical delivery across research, engineering, and cloud/platform teams.

Location: New York, New York, United States (onsite)
Pay: $96,130 - $181,944 USD (yearly)
Hours: 40
Experience: 5+ years
Education: Undergraduate degree required

Responsibilities

• Lead cross-functional teams of machine learning scientists, machine learning engineers, data scientists, and software engineers to deliver production-grade AI solutions.
• Partner with stakeholders to identify, refine, and sometimes reject opportunities to build machine learning products; collaborate with support functions including risk, technology, and model risk management, and incorporate relevant interfacing features.
• Translate business objectives into technically feasible AI/ML solutions and clearly articulate trade-offs across architecture, scalability, risk, implementation complexity, and model performance.
• Own the vision, strategy, and roadmap for AI technical products and capabilities, aligning with business objectives and advancing TD’s AI research and development.
• Maintain deep understanding of solution architecture and implementation details to challenge design decisions, assess delivery trade-offs, and prioritize work aligned to business and technical requirements.
• Facilitate professional and technical growth through mentorship and feedback.
• Anticipate resource needs across 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, including informed challenge on model design, MLOps, data architecture, and production readiness.
• Define cloud-native AI/ML solution architectures and operational strategies, partnering with engineering and architecture teams to ensure 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 business value by connecting model performance to measurable outcomes and enterprise AI value targets.
• Leverage deep technology expertise to ensure organization-wide provisioning, management, and support functions have the tools, processes, and documentation needed to execute on their mandates.
• Execute on engineering strategy related to introducing tools and automating build, test, release, and configuration activities across Application, Platform, and Infrastructure.
• Partner with Operations to integrate with appropriate tools and processes as part of automated or self-serve releases for Application, Platform, or Infrastructure.
• Keep pace with trends, frameworks, products, and applications through partnerships with vendor platform providers and engineering peers.
• Act as an expert and change agent for programs and services under management, including helping implement best practices and serving as an ambassador for ML Engineering.
• Manage stakeholder engagement and enterprise impacts, interpret client needs, assess engineering requirements, and identify solutions to non-standard requests.
• Participate in research projects and support presentations or communications for management or broader audiences.
• Monitor service and productivity, assess efficiency, implement continuous improvements, and lead remediation actions for performance, risk, or governance issues.
• Exercise discretion in managing correspondence and confidential information, escalating issues where appropriate; support compliance with internal and external requirements such as financial controls and segregation of duties.
• Stay current on emerging issues, trends, and evolving regulatory requirements and assess potential impacts.
• Contribute to a positive work environment that promotes service to the business, quality, innovation, and teamwork, including timely communication of issues and progress.
• Act as a brand ambassador for your business area/function and 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 including 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 with engineering and research teams.
• Strong technical proficiency in Python and SQL, plus 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 CS, Artificial Intelligence, Machine Learning, Data Science, Engineering, Mathematics, or a related field, or equivalent practical experience delivering complex AI/ML solutions.
• Undergraduate degree required.

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
• Base salary and variable compensation/incentive awards (eligibility for cash and/or equity incentive awards)
• Access to an online learning platform and a variety of mentoring programs
• Training and onboarding sessions

Additional Information

• Regular development conversations and training programs, plus a competitive benefits plan.
• Interview process: candidates of interest will be contacted to schedule an interview; outcomes communicated by email or phone call.
• Accommodation email: [email protected]
• Travel: Domestic travel occasional; International travel never.