Q

Quandri

Automates repetitive tasks for insurance brokerages

Senior Product Manager - AI Operations

Technical Product ManagementConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • At least 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: connect processes, information flows, tools, incentives, and human decisions, and recognize when improving one task merely shifts the bottleneck.
  • Sustained use of AI in personal work and experimentation with foundation models, agent frameworks, and development tools; explain lessons, adaptations as capabilities evolved, and relevant limitations.
  • Practical experience building and deploying AI workflows that people use, including explaining how they work, where they fail, how they are evaluated, and their business impact.
  • Technical fluency with model selection, context and retrieval, tool use, agent orchestration, APIs, evaluation, and access controls; ability to build and debug prototypes and make informed architecture and build-versus-buy decisions with engineers. Daily production coding is not required.
  • Product judgment to distinguish demonstrations from recurring problems worth solving, simplify processes before automating them, and determine when broader redesign is justified.
  • Track record of scaling adoption and changing how people work, including helping teams build skills and take ownership of new workflows.
  • Analytical discipline and ability to measure value against a baseline, including the human effort required to review and correct outputs.
  • Ability to align functional leaders, collaborate with technical teams, and explain new ways of working to colleagues with different levels of AI experience.

Responsibilities

  • Work directly with teams to understand goals, processes, handoffs, information needs, and recurring bottlenecks; challenge assumptions about necessary steps, human judgment, and responsibilities; design better workflows with functional leaders, establish a baseline, and prioritize by business impact, feasibility, and ongoing operating cost.
  • Stay current with foundation models, agent infrastructure, and AI development tools through hands-on experimentation; evaluate capabilities against real workflows for quality, reliability, speed, cost, and data requirements; determine when new capabilities warrant a different approach.
  • Translate company priorities and employee research into a focused roadmap for shared AI capabilities and adoption, balancing business value, technical and security requirements, and varying levels of AI maturity; make build, buy, reuse, and stop decisions and deliver in short cycles with continuous feedback.
  • Build working prototypes and pilot workflows using AI development tools, models, agents, APIs, and automation; connect information and tools, test with employees, and iterate based on observed failures; partner with Engineering to move validated solutions into dependable production use.
  • Define evaluation criteria, human review points, failure handling, monitoring, and ownership for each workflow; involve Engineering, Security, and Legal early on permissions, data privacy, integration quality, and production readiness; ensure people can understand automation actions and recover from failures.
  • Shape reusable capabilities for connecting agents to tools and knowledge, managing context and permissions, evaluating outputs, and monitoring execution; partner with Data on trustworthy information and access; turn successful experiments into maintained capabilities.
  • 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; diagnose and address barriers when adoption stalls.
  • Track adoption, time saved, cycle time, output quality, errors, and total operating cost; validate productivity gains with users and workflow evidence; use findings to improve, expand, or retire solutions and inform leadership.

Desired Qualifications

  • Experience with B2B SaaS operations, CRM and support systems, or internal knowledge tools.
  • Experience building a network of functional champions.
  • Experience taking an automation from one team’s experiment to a capability used across a company.
  • Familiarity with insurance workflows.

About the Company

Quandri

Automates repetitive tasks for insurance brokerages

Quandri builds customized digital workers, or software bots, that automate repetitive and time-consuming tasks for insurance brokerages. These bots handle routine operations such as quoting and suspending policies, enabling agencies to save time and money and free human staff for more valuable tasks. The product works by developing, deploying, and maintaining automation solutions tailored to an insurance workflow; clients pay for the initial setup plus ongoing support, creating a predictable revenue model. Quandri differentiates itself by offering tailored, end-to-end automation for the insurance brokerages sector, providing cost savings that are typically smaller than hiring more staff but with ongoing service to ensure smooth operation. The company's goal is to increase efficiency and reduce operating costs for insurance agencies by handleing repetitive tasks automatically, helping them grow and improve customer satisfaction.

Company size
51-200
Company stage
Series A
Total funding
$22M
Headquarters
Vancouver, Canada
Founded
2020

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