Lead Product Manager, Enterprise AI & Automation

MQ Referrals Only•Vancouver, BC

2d•CA$113,800 - CA$142,300•Remote

About The Position

We're looking for a Lead Product Manager, Enterprise AI and Automation to define how Marqeta identifies, governs, and scales AI and automation across the organization, focusing on the internal enterprise platform and employee-facing tooling to drive value across Marqeta’s internal operations and employee experience. You'll collaboratively design the AI vision for the enterprise, partner to build the governance and adoption frameworks that make it real, and work hands-on to evaluate tools, run pilots, and drive measurable business value. You'll partner closely with business unit leaders, the AI Steering Committee, and BT engineering to ensure AI initiatives are prioritized against the highest-impact problems — and that what gets built is driven to adoption. This is not a research role or a strategy-only role. The right person is equally comfortable presenting an AI governance framework to the executive team and rolling up their sleeves to configure an agent, evaluate a vendor, or run a pilot with a business unit. We work Flexible First. This role can be performed remotely anywhere within Ontario or British Columbia, Canada. We’d love for you to join us! This position is for an existing vacancy.

Requirements

7+ years in IT product management, with at least 2 years focused on AI or emerging technology products.
A genuine understanding of AI capabilities — summarization, classification, generation, agentic workflows — and the ability to translate that into business strategy and concrete use cases.
Experience building internal-facing enterprise AI systems or platforms; you think in systems and adoption curves, not just features.
Demonstrated experience managing product backlogs, defects, and enhancement roadmaps for enterprise applications or internal platforms.
Strong prioritization instincts and the ability to make hard calls about what matters most in a fast-moving, resource-constrained environment. You think in terms of business outcomes, not activity.
Demonstrated ability to influence without authority — across business units, finance, legal, and security stakeholders.
Comfort operating hands-on with AI tooling: you don't need to write production code, but you should be able to evaluate a platform, configure an agent, and troubleshoot when something isn't working.
Change management experience — you know how to drive adoption, not just deployment.

Nice To Haves

Experience with no-code/low-code AI platforms (e.g., Workato, or similar) is a strong plus.
Fintech or regulated industry experience preferred.

Responsibilities

Collaboratively define Marqeta's Enterprise AI & automation strategy, including responsible AI principles, governance frameworks, and a multi-quarter adoption roadmap.
Partner across the business to identify and prioritize high-value AI and automation use cases for each business unit.
Map how work flows across the enterprise to identify where AI can deliver step-change improvements — not just incremental automation, but fundamentally better ways of operating.
Define functional and technical requirements (FRD/TRD) for all internal AI/automation platforms, serving as the primary proxy for the BT engineering team.
Partner with Architecture Review Boards and AI Steering Committee to ensure all AI platforms and solutions adhere to defined IT standards and governance frameworks.
Design and lead the Plan and Commit process with business stakeholders for AI and automation technology requests.
Define criteria for prioritizing automation projects and enhancements based on strategic alignment, risk reduction, ROI, and operational impact.
Conduct feasibility assessments and cost-benefit analysis for proposed AI initiatives.
Prioritize the AI portfolio strategically against business outcomes and executive guidance — and hold the line on priorities to ensure engineering time is focused on the highest-impact work.
Maintain a visible backlog of active and pending AI and automation initiatives, giving stakeholders transparency into status, effort, and expected value.
Oversee QA and UAT processes to ensure systems meet business requirements before deployment.
Participate in sprint demos and provide feedback to ensure development meets defined acceptance criteria.
Coordinate deployment windows to minimize business disruption and risks.
Establish hypercare programs for major system go-lives, including elevated incident SLAs and daily status check-ins during stabilization.
Develop Business Systems Product Management competencies across the BT organization to ensure consistent and high-quality product practices across the BT organization.
Evaluate emerging AI technologies and conduct pilots to validate applicability and business value before committing to scale.
Lead vendor selection and commercial negotiations in partnership with Procurement and relevant stakeholders, and own ongoing vendor management for all Enterprise AI tooling contracts.
Stay current on the rapidly evolving AI landscape — tools, models, and platforms shift constantly, and this role requires continuous learning to ensure Marqeta's strategy reflects what's actually possible today.
Own and execute the Enterprise AI rollout plan in partnership with Corporate Communications and Learning and Development, ensuring deployment is phased, governed, and adoption-focused across all business units.
Drive change management for AI initiatives — the best solution is worthless if nobody uses it. Build the channels, rituals, and feedback loops that make AI visible and valued across the organization.
Establish and manage the internal AI organizational capability—including an AI Champion Network and Community of Practice—to drive centralized knowledge sharing, collective skill development, and accelerated, peer-led adoption across all business units.
Define adoption metrics and post-launch stabilization plans for all major AI deployments.
Design and own the Enterprise AI metrics framework; measure, report, and continuously improve the quantifiable business value delivered by the AI program.
Report progress and outcomes to the senior leadership on a regular cadence.
Establish governance guardrails that balance innovation velocity with responsible, compliant AI use — particularly important in a regulated fintech environment.