Director, AI Technology Delivery

Address:

Hybrid Work Model

About the Role

As Director Technology Delivery Lead you are accountable for the end-to-end technology delivery of Em BMO InvestorLines premier AI agent. You will lead the integrated delivery system across AI engineering wealth platforms Microsoft Azure data architecture quality engineering cybersecurity operations and third parties to deliver safe resilient scalable and measurable client outcomes.

You will operate as the principal technology counterpart to the Product Owner for Em. Together you are jointly accountable for overall delivery: the Product Owner leads product vision client value priorities and acceptance; the Technology Delivery Lead owns the integrated technology plan engineering execution technical quality production readiness and predictable delivery. This role is not a program coordination position. It is a hands-on senior technology leadership role with clear decision rights and accountability for outcomes.

Mandate and Decision Rights

• Own the integrated technology delivery plan for Em from discovery and architecture through build evaluation release operation and continuous improvement.
• Jointly commit scope sequencing release outcomes and delivery forecasts with the Product Owner balancing client value technical feasibility risk cost and capacity.
• Make or escalate timely technology delivery decisions across architecture engineering environments data integration testing controls and production readiness.
• Hold delivery teams and partners accountable for agreed outcomes quality standards dependencies and evidence-based release criteria.
• Protect the product from unmanaged technical debt fragmented ownership late-stage controls and delivery practices that are not fit for AI.

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 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-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 CI/CD 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.
• Exceptional leadership communication facilitation negotiation and talent-development skills.

Qualifications and Experience

• 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 AI/ML or generative AI solutions in production ideally involving multi-agent systems or other complex AI-enabled client experiences.
• Significant experience with Microsoft Azure and modern software engineering practices in a regulated enterprise.
• Experience in wealth management brokerage digital investing banking or similarly regulated financial services is strongly preferred.
• Proven experience leading cross-functional internal and partner teams through complex dependencies and high-stakes releases.
• University degree in Computer Science Engineering Information Systems; relevant advanced degree or certifications are considered assets.

Leadership Expectations

• Act as an owner: accountable for outcomes not activity or coordination alone.
• Put clients safety quality and sustainable value ahead of pace without evidence.
• Create clarity: 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 candour transparency inclusion disciplined follow-through and continuous learning.

The above represents BMO Financial Groups pay range and type.

Salaries will vary based on factors such as location skills experience education and qualifications for the role and may include a commission structure. Salaries for part-time roles will be pro-rated based on number of hours regularly worked. For commission roles the salary listed above represents BMO Financial Groups expected target for the first year in this position.

BMO Financial Groups total compensation package will vary based on the pay type of the position and may include performance-based incentives discretionary bonuses as well as other perks and rewards. BMO also offers health insurance tuition reimbursement accident and life insurance and retirement savings plans. To view more details of our benefits please visit: Us

At BMO we are driven by a shared Purpose: Boldly Grow the Good in business and life. It calls on us to create lasting positive change for our customers our communities and our people. By working together innovating and pushing boundaries we transform lives and businesses and power economic growth around the world.

As a member of the BMO team you are valued respected and heard and you have more ways to grow and make an impact. We strive to help you make an impact from day one for yourself and our customers. Well support you with the tools and resources you need to reach new milestones as you help our customers reach theirs. From in-depth training and coaching to manager support and network-building opportunities well help you gain valuable experience and broaden your skillset.

To find out more visit us at is committed to an inclusive equitable and accessible workplace. By learning from each others differences we gain strength through our people and our perspectives. Accommodations are available on request for candidates taking part in all aspects of the selection process. To request accommodation please contact your recruiter.

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