Audit Innovation Principal, AI

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!

What you will be doing

-

Embed directly with domain experts and partner accounting and audit firms, shadowing live engagements to understand practitioners' challenges firsthand, identify opportunities for automation, and define what quality and value creation look like within those workflows.

-

Build and take ownership of the solution that delivers value — whether that involves developing a new agent, enhancing an existing capability, or making the case for new platform functionality. Accountability extends to the outcome, not solely the requirements documentation.

-

Partner closely and on an ongoing basis with engineering and applied science teams to deliver committed outcomes — translating proposed solutions into scoped requirements and acceptance criteria, and collaborating throughout the build and iteration process.

-

Own the practitioner-credibility standard for AI outputs within the relevant domain — determining whether an output is suitable for use by a practising auditor, not merely whether it is technically accurate.

-

Contribute field-based insight to the evaluation framework, helping to define acceptable quality standards for audit-context outputs and identifying where existing benchmarks are absent or inaccurate.

-

Maintain close awareness of developments in AI — including emerging agent patterns and model capabilities — to ensure product judgment remains grounded in what is currently feasible, rather than solely what would be desirable.

-

Travel to and embed with client or partner-firm teams as required, in order to observe workflows directly.

What you will bring

-

A minimum of eight years of practice experience in audit, assurance, or accounting, sufficient to distinguish between output that is technically compliant and output that a qualified practitioner would be prepared to sign off on.

-

A well-formed perspective on quality standards — the ability to articulate and defend the threshold an AI output must meet in an audit context, and to translate that judgment into criteria that others can apply consistently.

-

A demonstrated, hands-on history of building a solution using AI or software to address a genuine problem — for example, a personal automation, an internal tool, a prompt-based workflow, or a side project. Supporting evidence, such as a demo or repository, is preferred over a verbal account alone.

-

Working technical fluency with modern AI tools, including large language models and prompt engineering, and potentially low-code agent builders or basic scripting — sufficient to independently build a working solution and to distinguish a model limitation from a process limitation.

-

The ability to operate credibly in both audit and technical settings, engaging with domain experts and engineers within the same week and being regarded as credible by each, on the basis of substantive experience on both sides.

-

A high degree of personal initiative and a bias toward building — a preference for producing a working version to demonstrate rather than describing a concept in documentation, together with the persistence to refine it until it performs reliably in a live workflow.

-

A genuine willingness to operate in ambiguity — this role has no established playbook, and part of the mandate is to develop the first one.

-

Preferred: a track record of introducing tooling or automation into a traditional firm or team, whether through formal or informal channels — this is regarded as a strong positive indicator for this role.