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 not only streamline workflows but also enhance audit quality and generate actionable insights for auditors and clients. Through harnessing the power of cutting‑edge tools, we aim to transform traditional audit practices into dynamic, forward‑thinking processes that are built for the complexities of the modern business environment. Our team, supported by KPMG’s global network, serves as the driving force behind this transformative journey. Focused on innovation, this team is dedicated to engineering solutions today that anticipate the challenges and opportunities of tomorrow, ensuring that audit services remain at the forefront of technological progression.
The Role
As a Principal AI Engineer, you will play a pivotal role in transforming advanced AI concepts into impactful, production‑ready solutions within the Audit Technology team. You will lead a dedicated AI engineering squad, working closely with data scientists, data engineers, software developers, cloud architects, and audit professionals to build and scale AI‑driven systems that enhance audit quality, efficiency, and insight generation. From developing robust proof‑of‑concepts to deploying 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. In addition to technical leadership, you will shape the growth of the team—mentoring engineers, promoting best practices, and fostering a culture of collaboration, innovation, and continuous improvement.
Responsibilities
- Leadership & Mentorship: Lead a high‑performing AI engineering team comprising 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‑embedded 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 the operationalisation of AI governance frameworks to ensure regulatory and ethical compliance.
- Capability Building & Knowledge Sharing: Drive initiatives to enhance internal capabilities, empowering team members and the broader Audit Technology function with the skills and knowledge 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 (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 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.
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