Director, Wealth Digital AI Platforms & Technology Delivery

Application Deadline: 10/30/2026 Address: 33 Dundas Street West Job Family Group: Data Analytics & Reporting This role is HYBRID, requires senior leadership and hands on capability to build complex AI solutions. About the role: BMO Canada Wealth Digital is accelerating the adoption of Artificial Intelligence (AI) across its customer-facing digital platforms to enhance client experiences, improve operational efficiency, and enable innovative wealth management solutions. We are seeking an experienced AI Delivery Director to lead the planning, execution, and delivery of strategic AI initiatives that support the evolution of our Wealth Management digital capabilities. This is a senior delivery leadership role requiring the coordination of cross-functional teams across Engineering, Architecture, Security, Risk Management, Compliance, and Business organizations. The successful candidate will be responsible for driving the delivery of secure, compliant, and resilient Multi-Agent AI solutions that leverage enterprise AI capabilities, including the AI Gateway, Policy Engine, Identity Fabric, AI Registry, Guardrails Runtime, Model Context Protocol (MCP) services, and AI Observability platforms. The AI Delivery Director will provide end-to-end leadership across the delivery lifecycle, from intake, discovery, and prioritization through implementation, deployment, adoption, and business value realization by adopting and utilizing AI tools in SDLC. Working closely with engineering leaders, product owners, architects, and executive stakeholders, the role will ensure AI-enabled solutions are delivered on schedule, aligned with enterprise standards, and integrated effectively within the broader technology ecosystem. Key Responsibilities 1. End-to-End Technology Delivery

• Lead delivery across multiple cross-functional product and platform teams, establishing one integrated plan, clear critical path, transparent dependencies, and accountable owners.
• Convert the product roadmap into executable technology increments, release plans, capacity models, milestones, and outcome-based commitments.
• Drive delivery discipline across scope, schedule, cost, quality, resources, risk, and benefits, using forecasts rather than artificial certainty.
• Identify constraints early, remove impediments, resolve cross-team trade-offs, and escalate decisions with clear options and recommendations.
• Ensure each release has explicit entry, exit, acceptance, operational readiness, and rollback criteria. 2. AI Engineering and

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Leadership

• Apply deep knowledge of generative AI and agentic systems, including LLM orchestration, tool use, retrieval-augmented generation, prompt and context engineering, evaluations, guardrails, memory, and human oversight.
• Embed full-lifecycle

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practices across requirements, design, build, test, evaluation, deployment, monitoring, and model or prompt change management.

• Ensure AI quality is measured using fit-for-purpose evaluations covering safety, groundedness, relevance, accuracy, latency, reliability, and client experience.
• Champion specification-driven development, automation, reusable engineering patterns, and AI-assisted software delivery where approved.
• Ensure deterministic software testing and probabilistic AI evaluation are integrated into CI/CD and release decisions. 3. Azure, Architecture and Platform Integration
• Provide senior technical leadership for solutions deployed on Microsoft Azure and integrated with BMO InvestorLine and Wealth Management platforms.
• Partner with solution, enterprise, security, data, and platform architects to maintain an approved, scalable target architecture and prevent local optimization or avoidable technical debt.
• Ensure APIs, event flows, data services, identity, access, observability, resilience, and non-functional requirements are designed and delivered end to end.
• Drive environment readiness, infrastructure as code, automated deployment, telemetry, performance engineering, capacity planning, disaster recovery, and production support readiness.
• Promote reuse of enterprise AI capabilities, shared services, patterns, and controls while preserving clear service boundaries and ownership. 4. Advanced Agile and Value-Stream Delivery
• Establish and continuously improve an advanced Agile operating model organized around persistent, cross-functional teams and measurable client or business outcomes.
• Lead portfolio and product-level planning, backlog readiness, dependency management, release forecasting, and flow optimization across multiple teams.
• Use evidence-based metrics such as lead time, cycle time, throughput, work in progress, predictability, escaped defects, reliability, evaluation performance, and value realization.
• Reduce handoffs, unnecessary governance, meeting load, and blocked work; create fast decision paths and clear single-point accountability.
• Coach delivery leaders, Scrum Masters, engineering leads, and teams in modern product delivery, DevSecOps, continuous delivery, and learning-driven retrospectives. 5. Quality, Risk and Responsible AI
• Build quality, privacy, security, regulatory compliance, model risk, accessibility, and responsible AI requirements into delivery from inception, not as release-end checkpoints.
• Partner with Legal, Risk, Compliance, Cybersecurity, Privacy, Model Risk, Data Governance, and Technology Risk to establish proportionate controls and auditable evidence.
• Ensure full-spectrum observability across application, infrastructure, data, model, prompt, agent, safety, and client-experience performance.
• Lead incident response, root-cause analysis, corrective action, and learning reviews for technology or AI quality events.
• Maintain transparent risk, issue, dependency, and decision records and ensure material risks are escalated promptly. 6. Stakeholder, Financial and Partner Leadership
• Serve as the senior technology delivery voice for Em with InvestorLine, Wealth, Technology & Operations, Applied AI, governance partners, and executive forums.
• Provide concise, fact-based reporting on outcomes, delivery confidence, risks, financials, quality, and decisions required.
• Own technology delivery financial stewardship, resource planning, vendor performance, commercial dependencies, and delivery commitments.
• Lead co-build and vendor engagements with clear accountability, knowledge transfer, architecture compliance, security obligations, and measurable outcomes.
• Create an inclusive, high-accountability environment that develops leaders, strengthens technical depth, and keeps teams focused on client outcomes. Key Success Measures
• Predictable delivery of roadmap outcomes and releases, with transparent forecast accuracy and controlled scope change.
• Measurable improvement in delivery flow, engineering productivity, automation, quality, and time to value.
• AI evaluation thresholds and non-functional requirements met before release and sustained in production.
• Stable, secure, resilient production performance with effective observability, incident management, and continuous improvement.
• Clear ownership, faster decisions, fewer cross-team handoffs, and effective dependency resolution.
• Measurable client adoption, experience, business value, and risk outcomes delivered in partnership with the Product Owner.
• Effective financial, capacity, vendor, and technical-debt management. Required Skills and Competencies
• Expert-level technology delivery leadership in large, complex, regulated environments, with accountability for multiple teams and production outcomes.
• Deep practical knowledge of AI/ML and generative AI delivery, including agentic architectures, LLM application patterns, evaluations, guardrails, observability, and responsible AI controls.
• Strong knowledge of wealth management and digital investing platforms, including client journeys, advice or guidance experiences, market and portfolio data, trading-rel