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Bank of Montreal

Runs Canadian and US banks for people and firms

Director – Wealth Digital AI Platforms & Technology Delivery

Engineering LeadershipUpdated on Nov 10
Full-timeToronto, ON, Canada

About the Job

Requirements

  • Expert-level technology delivery leadership in large, complex, regulated environments, with accountability for multiple teams and production outcomes.
  • Deep practical knowledge of artificial intelligence, machine learning, and generative artificial intelligence delivery, including agentic architectures, large language model 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-related integrations, and regulatory considerations.
  • Advanced proficiency with Microsoft Azure cloud architecture and delivery, cloud-native engineering, APIs, integration, identity and access, data services, telemetry, resilience, and DevSecOps.
  • Mastery of advanced Agile, product operating models, value-stream management, Lean flow, Scrum or Kanban, portfolio planning, continuous integration and delivery, test automation, and evidence-based delivery metrics.
  • Ability to challenge constructively, make decisions under ambiguity, translate technical complexity into business implications, and influence senior executives.
  • Commercial and financial acumen, including technology investment, capacity, vendor, contract, and delivery-performance management.
  • Leadership, communication, facilitation, negotiation, and talent-development skills.
  • At least 15 years of progressive experience in technology delivery, engineering, architecture, platform, or product technology leadership, including senior leadership accountability.
  • Demonstrated success delivering enterprise-scale digital products or platforms from strategy through production and ongoing operation.
  • Demonstrated delivery of artificial intelligence, machine learning, or generative artificial intelligence solutions in production.
  • Significant experience with Microsoft Azure and modern software engineering practices in a regulated enterprise.
  • Proven experience leading cross-functional internal and partner teams through complex dependencies and high-stakes releases.
  • A university degree in Computer Science, Engineering, Information Systems, Business, or a related discipline.

Responsibilities

  • 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.
  • Apply knowledge of generative artificial intelligence and agentic systems, including large language model orchestration, tool use, retrieval-augmented generation, prompt and context engineering, evaluations, guardrails, memory, and human oversight.
  • Embed full-lifecycle artificial intelligence software development lifecycle practices across requirements, design, build, test, evaluation, deployment, monitoring, and model or prompt change management.
  • Ensure artificial intelligence 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 approved AI-assisted software delivery.
  • Ensure deterministic software testing and probabilistic AI evaluation are integrated into continuous integration and delivery and release decisions.
  • 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.
  • 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.
  • Build quality, privacy, security, regulatory compliance, model risk, accessibility, and responsible AI requirements into delivery from inception.
  • 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.
  • Serve as the senior technology delivery voice for engagement 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.
  • Deliver roadmap outcomes and releases predictably, with transparent forecast accuracy and controlled scope change.
  • Improve delivery flow, engineering productivity, automation, quality, and time to value measurably.
  • Meet AI evaluation thresholds and non-functional requirements before release and sustain them in production.
  • Maintain stable, secure, resilient production performance with effective observability, incident management, and continuous improvement.
  • Establish clear ownership, faster decisions, fewer cross-team handoffs, and effective dependency resolution.
  • Deliver measurable client adoption, experience, business value, and risk outcomes in partnership with the Product Owner.
  • Manage financials, capacity, vendors, and technical debt effectively.
  • Act as an owner accountable for outcomes, not activity or coordination alone.
  • Put clients, safety, quality, and sustainable value ahead of pace without evidence.
  • Make responsibilities, decisions, risks, dependencies, and delivery confidence visible.
  • Lead with technical credibility while empowering engineering and product leaders closest to the work.
  • Build trust through candor, transparency, inclusion, disciplined follow-through, and continuous learning.

Desired Qualifications

  • Experience in wealth management, brokerage, digital investing, banking, or similarly regulated financial services.
  • Experience in multi-agent systems or other complex AI-enabled client experiences.
  • Relevant advanced degree or certifications.
  • Experience in the Wealth Management or Capital Markets AI space, or at financial institutions or technology firms.
  • Technology perspective and ecosystem experience in investment, securities, or trading.

About the Company

Bank of Montreal

Runs Canadian and US banks for people and firms

Bank of Montreal, known as BMO, runs a Canadian bank and a large American bank, serving households and businesses on both sides of the border. Until the Bank of Canada opened in 1935, it acted as the Canadian government's banker. People use its branches and apps for accounts, mortgages, credit cards, and investments, while companies borrow from it and turn to its BMO Capital Markets arm to raise money and trade securities. BMO makes money on the gap between the interest it charges on loans and pays on deposits, plus fees from managing wealth and advising on deals. Bank of Montreal was founded in 1817 as Canada's first bank, and it keeps its legal head office in Montreal and its executive offices in Toronto, Canada.

Company size
10,001+
Company stage
IPO
Headquarters
Toronto, Canada
Founded
1817

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