Audit Innovation Principal, AI

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.  

• 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.