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Toronto, Ontario, Canada
Hybrid
We are seeking a practising auditor who has developed a genuine interest in AI and has taken the initiative to build practical solutions. This work may have been undertaken independently, outside of core responsibilities, or introduced informally within a firm and subsequently adopted more broadly. In either case, the candidate will have taken the initiative to develop a solution rather than waiting for one to be provided.
This role applies that initiative directly within our AI Pioneer Program. The successful candidate will work alongside practitioners and partner firms, embedded in live audit workflows, translating identified pain points — such as inefficient or unreliable processes — into solutions that they scope, build, and own, in close collaboration with our engineering and applied science teams. This is not a liaison role; it requires genuine experience on both sides of the audit/technology divide, sound judgment on which ideas will hold up in a real audit environment, and a willingness to build the solution directly rather than delegate it. A central part of the role is defining what constitutes an acceptable standard in this domain — not accuracy in the abstract, but the threshold an output must meet before a qualified practitioner would be comfortable attaching their name to it. This standard does not yet exist in a formalized way for audit work, and the successful candidate will play a role in establishing it.
A formal engineering background is not required for this role. We are looking for curious practitioners who are willing to learn what is necessary to deliver working solutions — initiative and aptitude matter more than formal credentials, and the successful candidate will be supported by a dedicated team and platform to act on that initiative.
❗This a full-time, permanent position
❗This a new vacancy
❗This role is hybrid. You will be required to work from our Toronto office 3-days a week, located at 351 King Street East, Toronto, ON
Talent Partner : Taha Usmani
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.
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.
Salary Range:
The annual base salary for this position is between $145,000 CAD and $165,000 CAD per year.
This role is also eligible for discretionary bonus and/or commission, as well as other benefits. Actual pay within the listed range will be determined based on factors such as transferable skills, relevant experience, market conditions, and primary work location. The posted range is subject to change and may be updated periodically.