Director, wealth digital ai platforms

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 AI SDLC 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 AI SDLC 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 Investor Line 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, Dev Sec Ops, continuous delivery, a