Senior Product Manager - AI

BMO•Toronto, ON

2d•CA$94,600 - CA$176,000•Hybrid

About The Position

The Senior Product Manager – AI will contribute to the success of the Service, Operations, and Support (SOS) organization, a technology department within T&O’s Engineering and Platforms. The SOS organization is the face of technology to internal end-users, including system & network operations, IT Help Desk, end-user asset deployment, and IT Service Management (ITSM) governance practices. The following responsibilities apply to the Senior Product Manager – AI. Leads the strategy, implementation and ongoing management of enterprise AI capabilities. Owns the end-to-end lifecycle from use case identification, feasibility assessment, product vision and roadmap through solution design, implementation, adoption, performance monitoring and continuous improvement. Partners with business, technology, data science, machine learning engineering, architecture, cybersecurity, risk, compliance and vendor teams to deliver scalable, secure, governed and measurable AI solutions that create business value. Additional responsibilities include: Defines the vision, success measures, roadmap and delivery priorities for Gen AI systems aligned to business and technology objectives. Assesses and prioritizes Gen AI use cases based on business value, technical feasibility, data readiness, implementation complexity, risk and adoption potential. Translates business needs into requirements, epics, user stories, acceptance criteria and implementation plans for cross-functional delivery teams. Leads solution design and implementation activities, including prompt workflows, knowledge retrieval, data pipelines, model integration, controls, testing and release readiness. Applies knowledge of large language models, machine learning, deep learning, retrieval-augmented generation, prompt engineering, model evaluation and data governance to guide delivery decisions. Ensures AI solutions meet enterprise expectations for responsible AI, privacy, security, data protection, auditability, accessibility, resilience and risk management. Drives implementation readiness, adoption, change management, communications, training and transition to operational support. Monitors post-implementation performance, including quality, accuracy, usefulness, adoption, reliability, model drift, user feedback, risk indicators and business outcomes. Builds effective relationships across business, technology, governance and vendor teams; removes blockers and communicates complex AI concepts in clear business language. Produces regular reporting and executive updates on roadmap progress, delivery risks, adoption, value realization, system health and continuous improvement opportunities. Operates at a group or enterprise-wide level as a specialist resource to senior leaders and stakeholders. Takes measured risks while protecting the bank by applying the Risk Management Framework, Risk Culture and approved Risk Appetite in alignment with policy documents, laws and regulations.

Requirements

Typically 7+ years of relevant experience and a post-secondary degree in technology, computer science, data science, engineering, business or a related field, or an equivalent combination of education and experience.
Seasoned professional with experience delivering technology, AI, or enterprise platform initiatives from concept through implementation and sustainment.
Proficient in product management, project delivery, technology business requirements, implementation planning and stakeholder management.
In-depth knowledge of ITSM and the ITIL methodology and its practices
Strong understanding of Generative AI, machine learning, deep learning, large language models, prompt engineering, retrieval-augmented generation, model evaluation and AI solution design.
Strong knowledge of responsible AI, trust, bias, ethics, privacy, security, risk management, and operational sustainment.
Knowledgeable in data wrangling, preprocessing, governance, visualization, data-driven decision making, computational thinking, programming concepts, ML algorithms, and model scaling
Expert analytical and problem-solving skills, including data-driven decision making
Expert communication, influence, and collaboration skills, with the ability to manage ambiguity and explain complex AI concepts to business and executive audiences
Expert verbal and written communication skills

Nice To Haves

Experience with ServiceNow platform is a strong asset

Responsibilities

Leads the strategy, implementation and ongoing management of enterprise AI capabilities.
Owns the end-to-end lifecycle from use case identification, feasibility assessment, product vision and roadmap through solution design, implementation, adoption, performance monitoring and continuous improvement.
Partners with business, technology, data science, machine learning engineering, architecture, cybersecurity, risk, compliance and vendor teams to deliver scalable, secure, governed and measurable AI solutions that create business value.
Defines the vision, success measures, roadmap and delivery priorities for Gen AI systems aligned to business and technology objectives.
Assesses and prioritizes Gen AI use cases based on business value, technical feasibility, data readiness, implementation complexity, risk and adoption potential.
Translates business needs into requirements, epics, user stories, acceptance criteria and implementation plans for cross-functional delivery teams.
Leads solution design and implementation activities, including prompt workflows, knowledge retrieval, data pipelines, model integration, controls, testing and release readiness.
Applies knowledge of large language models, machine learning, deep learning, retrieval-augmented generation, prompt engineering, model evaluation and data governance to guide delivery decisions.
Ensures AI solutions meet enterprise expectations for responsible AI, privacy, security, data protection, auditability, accessibility, resilience and risk management.
Drives implementation readiness, adoption, change management, communications, training and transition to operational support.
Monitors post-implementation performance, including quality, accuracy, usefulness, adoption, reliability, model drift, user feedback, risk indicators and business outcomes.
Builds effective relationships across business, technology, governance and vendor teams; removes blockers and communicates complex AI concepts in clear business language.
Produces regular reporting and executive updates on roadmap progress, delivery risks, adoption, value realization, system health and continuous improvement opportunities.
Operates at a group or enterprise-wide level as a specialist resource to senior leaders and stakeholders.
Takes measured risks while protecting the bank by applying the Risk Management Framework, Risk Culture and approved Risk Appetite in alignment with policy documents, laws and regulations.