Technical Product Owner - AI & Machine Learning Solutions

TD•Toronto, ON

3d•CA$140,000 - CA$240,000•Onsite

About The Position

Layer 6 is the AI research centre 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 always looking for people driven to be at the cutting edge of machine learning in research, engineering, and impactful applications. 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.

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.
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.

Responsibilities

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.