Manager, AI Delivery

About QuadReal Property Group

QuadReal Property Group is a global real estate investment, development and operating company headquartered in Vancouver, British Columbia. Its assets under management are $98.5 billion. From its foundation in Canada as a full-service real estate operating company, QuadReal has expanded its capabilities globally for investments in equity and debt in both the public and private markets. QuadReal invests directly through operating platforms in which it holds an ownership interest and via programmatic partnerships.

QuadReal seeks to deliver strong investment returns while creating sustainable environments that bring value to the people and communities it serves. Now and for generations to come.

QuadReal: Excellence lives here.

www.quadreal.com

Role Description

The AI & Digital Innovation team builds and runs AI products used across QuadReal, from document intelligence and underwriting support to agentic workflows connected to our core platforms. The team is small and senior, spanning AI architecture, engineering, business analysis, program management and business engagement, and it moves at the pace of a technology that keeps changing underneath it. What it needs is a manager who owns delivery of the portfolio end to end, makes the prioritization calls, and raises the technical bar without becoming the bottleneck.

This is the connective role on the team. AI architecture sets the technical direction, business engagement brings the demand, and delivery has to tie the two together into working software that people actually use. This individual is the glue: the single point of accountability for what ships, when, at what quality, and whether the business got the value it was promised.

This is a functional manager, not a pure people leader. This individual will be in the work: reviewing designs and requirements, sitting in stakeholder sessions, building the value case, and deciding what gives when two initiatives need the same person in the same week. Using AI day to day is central to the job, not a side interest. Intimate, hands-on knowledge of how these tools behave is what makes the judgment calls sound and what earns credibility with our stakeholders. They are deliberately hands-off with senior specialists and hands-on with team members who are still growing.

The hardest part of this role is judgment. Business stakeholders bring more ideas than the team can build, and they do not arrive labelled. Some are valuable, some only sound urgent, some are both. This individual separates them, quantifies what is real, sequences the rest, and says no clearly enough that the relationship still works afterward.

This person will:

Own delivery of the active AI portfolio: scope, sequencing, quality, and the commitments made to the business

Manage and develop the delivery team (program and project management, business analysis, engineering) while working alongside senior specialists in AI architecture and business engagement

Make prioritization and trade-off calls across competing initiatives, and defend them to sponsors and leadership

Help business sponsors quantify the value of their initiative before build, and validate it after launch

Act as a credible technical counterweight in design, requirements and vendor discussions

Responsibilities

Delivery Ownership & Team Leadership (30%)

Own delivery outcomes across the portfolio, not just delivery activity: what shipped, whether it works, and whether the business is using it

Manage, coach and develop the delivery team; set clear expectations and give direct feedback early rather than at review time

Set the priorities and outcomes the Project Manager runs the delivery cadence against, and clear the obstacles they escalate

Own the portfolio roadmap, capacity plan and budget-versus-actual through the Program Manager, and make the capacity and sequencing calls yourself

Distribute ownership so no initiative is single-threaded through one person, including this role

Work hands-off with senior specialists: pressure-test the thinking, make the call, then get out of the way

Hold a consistent quality bar across requirements, design reviews, accuracy and evaluation testing, and release readiness

Stay in the work: prototypes, prompt and agent design, code and design reviews, stakeholder sessions, documentation

Prioritization & Value Definition (25%)

Screen incoming AI requests against the team intake framework: data readiness, technical feasibility, business readiness, minimum viable accuracy, output verifiability, and build versus buy

Make and communicate sequencing decisions when initiatives compete for the same capacity, and revisit them as conditions change

Partner with business sponsors to quantify value in their own terms (time saved, cost avoided, revenue enabled), then hold the baseline after launch

Defer or stop work that will not pay off, and explain the reasoning in a way the sponsor can accept and repeat

Build a roadmap where initiatives reinforce each other: shared components, common data foundations, and sequencing that compounds rather than competes

Design with cost in mind: model AI consumption cost into solution design and flag early when an approach will not scale economically

Technical Judgment & Solution Quality (25%)

Provide consistent technical direction across initiatives: LLM application patterns, agentic workflows, retrieval and context design, API and MCP integration, evaluation, and human-in-the-loop design

Interrogate requirements rather than transcribe them: decompose compound asks, surface conflicting requirements, and force priority calls before build starts

Own delivery discipline across the development lifecycle: environments, source control, code review, CI/CD, release management, and handoff to run and support

Make sure solutions are instrumented so adoption and benefit can be measured rather than estimated

Operate inside enterprise guardrails: AI governance tiers, security and data access requirements, architecture standards, and vendor assessment

Stakeholder & Vendor Management (20%)

Act as the credible face of delivery to the business, from analyst to executive, and the person stakeholders come to early rather than late

Translate technical constraints into business language, and business asks into buildable scope

Handle hard conversations well: timeline slips, scope cuts and feasibility limits delivered without damaging the relationship

Report progress, risk and realized value to leadership forums, including the AI Oversight Committee

Manage vendor and partner delivery: hold scope, quality and change control, and escalate commercial issues with evidence

Experience and Qualifications

Must-Have

5+ years delivering technology solutions, including 2+ years managing people or leading a delivery team

Real AI depth, not passing interest: you have shipped LLM or ML-based solutions and can hold your own on model selection, prompt and agent design, evaluation and accuracy trade-offs, and where AI is the wrong tool

You use AI tools daily and can show us what you have built with them

A track record of prioritizing under constraint: you have told a senior stakeholder no, explained why, and kept the relationship intact

Demonstrated ability to help a business partner quantify value, including honesty about where the numbers were soft

Experience owning capacity planning across competing initiatives, including the judgment to move people mid-flight and the willingness to make that call

High emotional intelligence and excellent communication: you read a room accurately, adjust to your audience, and can be direct without being abrasive

Experience managing within or as part of a software development project: environments, code review, CI/CD and release management

Comfortable being hands-off with senior experts and hands-on mentoring junior team mem