Senior Product Manager, AI Operations

Who we are
Our mission at Quandri is to transform insurance into a trusted and delightful experience using AI. We are building the AI operating system for North America’s best insurance agencies and brokerages.
For the first time ever, we have the technology to fundamentally change how insurance operates, and the stakes are human. Better insurance means families protected from harm, businesses able to take risks, and communities more resilient when the worst happens. For insurance practitioners, the ability to deliver this level of service finally becomes possible.
What we do
Quandri brings deeply specific, vertically-focused AI to the industry’s most critical stakeholders:insurance agencies and brokerages.
Agents are the lifeblood of the industry, the place where most property and casualty insurance is sold and serviced. Yet operating an agency is harder than ever. Peak product complexity, accelerating consolidation, a deepening talent crisis, and the AI revolution are all reshaping how agencies work.
Quandri sits at the cutting edge of this shift, rebuilding how agencies operate one workflow at a time. We pair a deep understanding of insurance processes, systems, and data with serious technical capability and proprietary models, so the transformation is tangible and operational rather than aspirational.
We’re making insurance better for policyholders, while helping insurance brokerages deliver a better client service, grow faster, and harness AI so that they control the future of insurance distribution.
Why join us?
You'll join a fast-moving team with real ownership, direct access to leadership, and a genuine hand in shaping the company's next chapter.
We are headquartered in beautiful Vancouver, BC, backed by leading US and Canadian investors, and the recognition has followed:
LinkedIn’s Top Canadian Startups in 2024 & 2025
Deloitte’s Fast 50
CB Insights’ Insurtech 50 and Fintech 100
The Globe and Mail’s Top Growing Companies in Canada.
About the Role
You will help reinvent how Quandri's teams operate by combining a deep understanding of their work with practical knowledge of what AI can do. We are looking for a systems thinker and hands-on builder who actively experiments with foundation models, agents, and emerging tools, recognizes when new capabilities make a different approach possible, and turns that insight into solutions people will use.
You will own two connected responsibilities: Quandri's internal AI platform and company-wide adoption. The platform will include shared tools, agent infrastructure, evaluation, and guardrails. Adoption means working with functional leaders to redesign workflows, build team capabilities, and make new ways of working effective and sustainable.
Your customers are our teams across Sales, Marketing, Customer Experience, Implementation, Finance, Operations, Product and Engineering. You will discover where work gets stuck, decide which problems are worth solving, and take solutions from prototype to sustained use. You will report to the VP of Product and partner closely with Engineering, the VP of Finance and Operations, Security, and functional leaders.
You should already use AI extensively in your own work and have a clear point of view, grounded in experimentation, on where it is useful and where it falls short. Your success here will show up in sustained, high-quality adoption, faster work, better decisions, and capacity returned to teams. You will also help colleagues become better builders through shared tools, practices, and support.
Why this Role Matters
You will help define how Quandri operates as AI changes what teams can accomplish. By redesigning workflows and building capabilities that compound across the company, you will help teams handle more complex work, improve quality, and spend more time on customers. You will see the impact directly in how your colleagues work every day.
What You'll Do:
Rethink how teams work. Work directly with teams to understand their goals, processes, handoffs, information needs, and recurring bottlenecks. Challenge assumptions about which steps are necessary, where human judgment adds value, and how responsibilities could change. Design better workflows with functional leaders, establish a baseline, and prioritize by business impact, feasibility, and ongoing operating cost.
Translate AI advances into practical opportunities. Stay current with foundation models, agent infrastructure, and AI development tools through regular hands-on experimentation. Evaluate new capabilities against real Quandri workflows, including their quality, reliability, speed, cost, and data requirements. Identify when an advance warrants a new approach and when an existing solution remains the better choice.
Own the platform and enablement roadmap. Translate company priorities and employee research into a focused roadmap for shared AI capabilities and adoption. Balance business value, technical and security requirements, and different levels of AI maturity across teams. Make clear build, buy, reuse, and stop decisions, and deliver in short cycles with continuous feedback.
Build and validate. Personally build working prototypes and pilot workflows using AI development tools, models, agents, APIs, and automation. Connect the information and tools needed to complete useful work, test with employees, and iterate on observed failures. Opportunities may span account research through sales preparation, support knowledge retrieval through response drafting, or implementation preparation through handoff. Partner with Engineering to take validated solutions into dependable production use.
Make solutions dependable. Define evaluation criteria, human review points, failure handling, monitoring, and ownership for each workflow. Involve Engineering, Security, and Legal early in decisions about permissions, data privacy, integration quality, and production readiness. Ensure people can understand what an automation did and recover when it fails.
Create reusable capabilities. Shape shared capabilities for connecting agents to tools and knowledge, managing context and permissions, evaluating outputs, and monitoring execution. Partner with Data, which owns shared analytics and the integrations roadmap, on trustworthy information and access. Turn successful team experiments into maintained capabilities that others can adopt without starting over.
Lead adoption and change management. Work with functional leaders to embed new workflows into daily operations, with clear responsibilities and human review points. Build a network of functional champions and use practical training, hackathons, office hours, and rollout support to help teams develop their own capabilities. When adoption stalls, diagnose whether the barrier is usefulness, trust, skills, or workflow fit, then adjust.
Measure business value. Track adoption alongside time saved, cycle time, output quality, errors, and total operating cost. Validate productivity gains with users and workflow evidence. Use those findings to improve, expand, or retire solutions and keep leadership informed about what is working.
The right person for this role will have:
6+ years of product management experience, ideally including internal products, platforms, or developer and employee tooling, with ownership from discovery through sustained use.
Systems thinking: you can connect processes, information flows, tools, incentives, and human decisions, and recognize when improving one task simply moves the bottleneck elsewhere.
A sustained practice of using AI in your own work and testing new foundation models, agent frameworks, and development tools. You can explain what you have learned, what you have changed as capabilities evolved, and which limitations still matter.
Practical experience building and deploying AI workflows. You can demonstrate something people use and explain how it works, where it fails, how you evaluate it, and the business impact it delivers.
Technical fluency with model selection, context and retrieval, tool use, agent orchestration, APIs, evaluation, and access controls. You can build and debug prototypes and make informed architecture and build-versus-buy decisions with engineers. Daily production coding is not required.
Strong product judgment: you can distinguish an interesting demonstration from a recurring problem worth solving, simplify a process before automating it, and decide when a broader redesign is justified.
A track record of scaling adoption and changing how people w

Employment type
Full-time

Work arrangement
Hybrid