Join us as a AI Engineering Lead
- In this key role, you’ll lead the engineering of AI and machine learning (ML) capabilities for the Economic Crime Hub, translating decision strategies into scalable, production-grade solutions
- We’ll look to you to own the end-to-end lifecycle of AI and ML models, from development and deployment through to monitoring, optimisation, and ongoing performance management
- This is an opportunity to make an impact by ensuring AI and ML solutions operate reliably and safely within governed, compliant environments, while meeting business, risk, and regulatory requirements
As a AI Engineering Lead, you'll lead the design, build, and deployment of ML models and AI systems into production environments. You'll translate decision strategies and analytical requirements into production-grade solutions, designing reusable pipelines and frameworks that support efficient delivery while ensuring reliable, high-quality outcomes that advance the Economic Crime Hub's objectives.
Moreover, you’ll establish and evolve ML engineering standards, tooling, and best practices across the Hub, while partnering closely with Analytics, Product, and Technology teams to deliver end-to-end decisioning capabilities. You’ll also drive the development of AI platform architecture and infrastructure to support the secure and efficient deployment of AI solutions, while providing technical leadership across ML engineering and AI disciplines to drive innovation, strengthen capability, and promote the adoption of effective solutions.
In addition, you’ll be:
- Owning the end-to-end model lifecycle, including deployment, monitoring, optimisation, retraining, and decommissioning
- Overseeing model performance in production, including accuracy, stability, drift, and real-world effectiveness
- Embedding controls, monitoring, and validation within AI and ML solutions to ensure safe and compliant decision-making
- Ensuring the technical integrity, resilience, and scalability of AI systems in alignment with enterprise architecture standards
- Ensuring models are explainable, auditable, and compliant with governance requirements, working in partnership with Model Risk and Assurance
- Enabling the automation of decision-making through AI and reducing reliance on manual intervention
- Building and leading a high-performing ML engineering capability, including hiring, development, and technical progression of team members
We’re looking for someone with extensive experience designing, building, and deploying production-grade AI and ML solutions, supported by a strong understanding of ML engineering, Machine Learning Operations (MLOps), and model lifecycle management. You’ll bring the technical expertise to translate analytical models and decision strategies into robust, operational systems that deliver business value within regulated environments.
To succeed in this role, you must also have a proven track record of developing AI platforms and deployment capabilities that support the secure, reliable, and efficient delivery of AI solutions. Equally important is the ability to provide technical leadership, collaborate across multidisciplinary teams, and drive innovation while maintaining strong governance and risk management practices.
In addition, you’ll need to demonstrate:
- Extensive experience designing and delivering production-grade ML and AI systems
- Deep expertise in ML engineering and model lifecycle management, including deployment, monitoring, optimisation, and ongoing performance management
- Strong knowledge of MLOps practices, including CI/CD, pipeline orchestration, automation, and production model management
- Proven ability to translate analytical models and decision strategies into robust, operational decisioning systems
- Experience developing AI platforms, scalable architectures, and reusable engineering components that enable efficient AI solution delivery
- Experience working in regulated environments, with a strong understanding of governance, explainability, model risk, and compliance requirements
- Proven leadership and stakeholder management skills, with the ability to collaborate across Analytics, Product, and Technology teams while building and developing high-performing technical teams
