Director, AI Technology Delivery

Application Deadline:

10/30/2026

Address:
250 Yonge Street

Job Family Group:

Data Analytics & Reporting

Hybrid Work Model

About the Role

As Director, Technology Delivery Lead, you are accountable for the end-to-end technology delivery of Em, BMO InvestorLine's 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.

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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.

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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, L