NatWest Group
AI Engineering Lead
How your CV stacks up
Upload your CV to see how well it fits this job role
?%
Join usasaAIEngineering 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.
What you'll do
As an 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.
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
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
The skills you'll need
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.
Get help with your application
Your very own career expert that helps elevate your application to the next level.
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
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Skills