Lead AI Engineer

Job summary

JOB DETAILS Location: London Capability: Audit Experience Level: Manager Type: Full Time Business Area: Audit Centre of Excellence (ACE) Contract type: Permanent JOB DESCRIPTION Base Location: London plus network of 20 offices nationally: The KPM*** Audit Technology team is dedicated to building cutting-edge solutions in close collaboration with the Audit function.

Qualifications

• Experience Level: Manager
• delivery timelines, and integration plans.
• Provide hands-on contributions to ensure AI capabilities are smoothly embedded within core audit platforms and services.
• Capability Building & Knowledge Sharing: Contribute to internal capability-building initiatives and empower team members and the broader Audit Technology function by sharing practical
• Experience: Significant professional experience in backend development as a Senior role.
• Languages/Frameworks: Strong proficiency in Python with hands-on experience in asynchronous programming, concurrency and multithreading.
• API Expertise: Proven experience in building and integrating APIs, with expertise in API documentation and schema definition using OpenAPI/Swagger.
• Strong understanding of RESTful API design, authentication/authorization standards, and API lifecycle best practices; familiarity with Microsoft Graph API is a plus.
• Experience & Knowledge
• Good knowledge of generative AI, machine learning, deep learning, natural language processing or 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.

Responsibilities

• As a crucial member of the team, you will collaborate with a talented mix of Cloud & DevOps Engineers, Product Owners/Managers, Solution Architects, Data Engineers, Business Analysts, and Testing Specialists.
• As a valued team member, you'll be expected to stay current with the tech field and the latest trends in Audit delivery.
• Lead by Example: Lead by example in a hands-on capacity, actively contributing to codebases and technical decisions while mentoring junior engineers.
• Scalable AI Engineering: Develop and deploy production-grade AI systems tailored to audit applications.
• Contribute to architectural decisions and solution governance.
• Write clean, efficient, and scalable code that adheres to software engineering principles, MLOps practices, and cloud-native development.
• AI Solution Delivery: Own the implementation of ML pipelines, APIs, and data integration workflows.
• Operational Excellence: Contribute to defining reusable development patterns, enforcing coding standards, and implementing MLOps best practices that support version control, performance optimisation, and maintainability.
• Cross-Disciplinary Collaboration: Work side-by-side with data scientists, product managers, platform engineers, and QA teams to align on technical

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