Technical Product Manager, Applied Science – AI Products

Caseware

Toronto

On-site

CAD 110,000 - 150,000

Full time

14 days+

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Job summary

Caseware, a longstanding Canadian fintech software leader, is seeking a Technical Product Manager to own AI across its platform. You will oversee core AI surfaces, including agents and agent builders, eval tooling, and the memory that powers continuous improvement.

You will champion auditor viewpoints, shadow workflows, and validate outputs against professional sign‑offs. As the primary product owner for AI quality, you decide what to build, in what order, and ensure it meets the bar before

Responsibilities

  • Own how Caseware builds, evaluates, and raises the quality of AI across the platform.
  • Lead the core AI surfaces: agents and agent builder practitioners, eval builder and eval roadmap, and agentic memory.
  • Act as SME proxy for auditors, shadow real workflows, validate outputs against practitioner expectations.

Job description

Caseware is one of Canada's original Fintech companies, having led the global audit and accounting software industry for over 30 years, with more than 500,000 users across 130 countries and available in 16 different languages. While you might not have heard of us (yet) over 36,000 accounting and audit professionals list Caseware as a skill on their LinkedIn profiles!

We're looking for a Technical Product Manager to own how Caseware builds, evaluates, and raises the quality of AI across the platform. You are the product owner of our core AI surfaces: the agents and agent builder practitioners use to do real work, the eval builder and eval roadmap that let us measure quality, and the agentic memory that lets the platform improve as it is used. You decide what we build, in what order, and whether it clears the bar before it reaches someone's audit file.

You are also the closest thing the product has to a practitioner in the room. Acting as an SME proxy, you carry the voice of the auditor into every decision: shadowing real workflows, validating outputs against what a qualified practitioner would actually sign off on, and turning \

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