Role Type: Hybrid working
The Team
The Audit Technology team at KPMG is driving innovation at the intersection of auditing and advanced technological solutions, reshaping the future of audit delivery. By combining expertise in Artificial Intelligence, Data Engineering, Data Analytics, and Software Development, the team is revolutionising the auditing process to deliver smarter, faster, and more reliable outcomes.
Our mission is to design and implement robust, intelligent, and scalable technologies that allow audit workflows to become more efficient, enhance audit quality, and generate actionable insights for auditors and clients. Through cutting edge tools, we aim to transform traditional audit practices into dynamic, forward thinking processes designed for today's complex business environment.
Supported by KPMG's global network, our team drives this transformative journey. Focused on innovation, we engineer solutions that anticipate tomorrow's challenges and opportunities, ensuring audit services stay at the forefront of technology.
The Role
As a Principal AI Engineer, you will transform advanced AI concepts into production ready solutions within the Audit Technology team. You will lead a dedicated AI engineering squad, collaborate with data scientists, data engineers, software developers, cloud architects, and audit professionals to build and scale AI driven systems that improve audit quality, efficiency, and insight generation.
From robust proof of concepts to enterprise grade solutions, you will apply your expertise in AI engineering, cloud platforms, and technologies such as Generative AI, Azure, and Databricks to embed intelligence into critical audit workflows and products.
Beyond technical leadership, you will shape team growth-mentoring engineers, championing best practices, and fostering a culture of collaboration, innovation, and continuous improvement. You will stay ahead of AI engineering trends, advocate modern development methodologies, and drive knowledge sharing across technology and audit domains.
Responsibilities
- Leadership & Mentorship: Lead a high performing AI engineering team composed of software engineers and AI practitioners. Provide hands on technical direction, foster career growth, and cultivate a collaborative culture that emphasizes engineering excellence, innovation, and continuous improvement.
- Scalable AI Engineering: Drive the design, development, and deployment of production grade AI systems tailored to audit applications. Ensure solutions are scalable, reliable, and maintainable by applying strong software engineering principles, MLOps practices, and cloud native development.
- End to End AI Solution Delivery: Oversee the full lifecycle of AI product engineering-from architectural design and prototyping to CI/CD enabled deployment-using modern platforms and tools such as Azure ML, Databricks, MLflow, LangChain, and LangGraph. Champion automation, testing, and observability across pipelines.
- Operational Excellence: Define reusable development patterns, enforce coding standards, and promote MLOps best practices that support version control, performance optimisation, and maintainability.
- Cross Disciplinary Collaboration: Partner closely with data scientists, product managers, platform engineers, and QA teams to align on technical requirements, delivery timelines, and integration plans. Ensure AI capabilities are well integrated within core audit platforms and services.
- AI Governance & Risk Management: Implement engineering controls to support responsible AI use, including model monitoring, explainability, security, and auditability. Contribute to operationalising AI governance frameworks to ensure regulatory and ethical compliance.
- Capability Building & Knowledge Sharing: Drive initiatives that enhance internal capabilities, empowering team members and the broader Audit Technology function with the skills and knowledge required to adopt and adapt AI innovations effectively.
Requirements
- Bachelor (preferably master or PhD) in Computer Science, Artificial Intelligence, Data Science, Statistics, Engineering, or a related technical field-or equivalent professional experience.
- Strong knowledge of generative AI, machine learning, deep learning, natural language processing, and other relevant AI fields.
- Proven track record of designing, developing, and deploying AI systems in production environments.
- Proficient in Python and key ML libraries (e.g., PyTorch, PySpark, scikit learn, Hugging Face Transformers).
- Hands on experience with modern data platforms and AI tooling such as Azure ML, Databricks, MLflow, LangChain, and LangGraph.
- Proven experience with modern engineering practices, including Git, version control, unit testing, and containerisation.
- Familiarity with agile work methodologies and tools like Jira and Confluence.
- Exceptional leadership and communication skills, with the ability to convey complex technical concepts to diverse audiences.
- Advanced certifications in AI, machine learning, cloud computing, or data engineering are highly advantageous.
- Professional accounting qualification preferred, however not a requirement.
Why Audit at KPMG?
Audit is the largest of our UK practices. Some of the world's biggest companies rely on us to provide independent insight, challenge, and expertise, so the work we undertake affects investment decisions, inspires confidence in public sector expenditure, and supports economic growth. Today, more than ever in disruptive times, audit is a function needed by society, and in the future, we can capitalise and grow. As part of the Audit team, you'll help build the confidence and trust that business and society need to thrive. We want to lead the conversation shaping the profession's future. With the scale and variety of our audit engagements globally, we're well placed to create change. If you share our commitment to achieving excellence, working to the highest audit standards, naturally collaborate, value different perspectives, and relish the opportunity to develop and progress, KPMG could be the place where you can thrive.