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- Tasks: Lead an AI engineering team to create impactful, production-ready solutions in audit technology.
- Company: Join a forward-thinking company at the forefront of AI innovation.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Dynamic environment with a focus on continuous improvement and knowledge sharing.
- Why this job: Shape the future of audit with cutting-edge AI technologies and collaborative teamwork.
- Qualifications: Expertise in AI, strong leadership skills, and experience with modern engineering practices.
The predicted salary is between 80000 - 100000 £ per year.
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, 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. You will stay at the forefront of AI engineering trends, advocate for modern development methodologies, and drive knowledge-sharing across both the technology and audit domains.
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.
Qualifications
- 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, 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.
Senior Manager - Principal AI Engineer employer: KPMG International Cooperative
Contact Detail:
KPMG International Cooperative Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Manager - Principal AI Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your AI projects, especially those that highlight your experience with tools like Azure and Databricks. This will give you an edge and demonstrate your hands-on expertise.
✨Tip Number 3
Prepare for interviews by brushing up on common AI engineering questions and scenarios. Practice explaining complex concepts in simple terms, as you'll need to communicate effectively with diverse teams.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Senior Manager - Principal AI Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the Principal AI Engineer role. Highlight your expertise in AI engineering, cloud platforms, and any relevant projects you've led or contributed to.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about AI and how you can contribute to our Audit Technology team. Share specific examples of your leadership and mentorship experiences that showcase your ability to drive innovation.
Showcase Your Technical Skills: Don’t forget to mention your proficiency in Python and key ML libraries, as well as your hands-on experience with tools like Azure ML and Databricks. We want to see how you’ve applied these skills in real-world scenarios.
Apply Through Our Website: We encourage you to apply directly through our website for a smoother application process. This way, we can easily track your application and get back to you faster!
How to prepare for a job interview at KPMG International Cooperative
✨Know Your AI Stuff
Make sure you brush up on your knowledge of generative AI, machine learning, and the specific tools mentioned in the job description like Azure ML and Databricks. Be ready to discuss your past projects and how you've applied these technologies in real-world scenarios.
✨Showcase Your Leadership Skills
As a Senior Manager, you'll need to demonstrate your ability to lead and mentor a team. Prepare examples of how you've successfully guided teams in the past, fostered collaboration, and promoted best practices in AI engineering.
✨Prepare for Technical Questions
Expect to face technical questions that assess your problem-solving skills and understanding of AI systems. Practice explaining complex concepts in simple terms, as you'll need to communicate effectively with diverse audiences, including non-technical stakeholders.
✨Emphasise Collaboration
Highlight your experience working cross-functionally with data scientists, software developers, and other professionals. Be ready to discuss how you've aligned technical requirements and delivery timelines in previous roles to ensure successful project outcomes.