Join us as a Machine Learning Engineer
- In this role, you'll drive the design, development and operationalisation of machine learning and AI solutions, enabling the delivery of resilient, scalable and innovative solutions using both Generative AI and traditional Machine Learning techniques
- Day-to-day, act as a technical expert, building and deploying production-grade ML and AI systems while championing engineering best practices across the team
- If you are ready for a new challenge and are passionate about turning cutting-edge machine learning and AI capabilities into robust business solutions, this could be the role for you
- We're offering this role at vice president level
What you’ll do
We're looking for someone who can work with our Data Scientists to understand business requirements and translate them into scalable machine learning and AI solutions. You'll work closely with data scientists, engineers, architects and business stakeholders to deliver reliable and secure products that support our business strategy and customer outcomes.
You'll stay curious about emerging technologies and industry developments, helping to identify opportunities to improve our machine learning and AI platforms, tooling and delivery capabilities.
You’ll also be responsible for:
- Designing, building, deploying and maintaining machine learning, Generative AI and Agentic AI solutions in production environments
- Implementing robust ML engineering and MLOps practices, including CI/CD pipelines, automated testing, model monitoring and observability
- Developing scalable data pipelines, feature engineering processes and reusable platform components to support model deployment and operation
- Working collaboratively within Agile, multi-disciplinary teams to deliver value through iterative development and continuous improvement
- Ensuring solutions meet engineering, security, governance, risk and regulatory requirements throughout the software and model lifecycle
The skills you’ll need
To be successful in this role, you'll need experience designing, building and supporting production machine learning and AI solutions within a modern engineering environment. You'll ideally hold an undergraduate or master's degree in a quantitative, engineering or computer science discipline, or demonstrate equivalent practical experience.
You'll also need strong software engineering experience, including cloud-native development, data engineering and machine learning operations. We'll look to you to demonstrate technical leadership, a passion for engineering excellence and the ability to mentor others while continually developing your own skills. Ability to self-manage, take initiative and demonstrate a proactive approach to delivering high-quality engineering outcomes is also required.
You’ll also need:
- Proven experience delivering complex machine learning and AI solutions into production and communicating technical concepts effectively to both technical and non-technical audiences
- Strong software engineering skills, including object-oriented programming, API development, testing frameworks, source control and modern development practices
- Experience developing, deploying and supporting Generative AI and Agentic AI solutions, including orchestration frameworks, evaluation methodologies and operational monitoring – as well as traditional Machine Learning techniques
- Expertise in MLOps practices, including model deployment, CI/CD, infrastructure as code, observability, model monitoring and performance optimisation. Knowledge of containerisation and orchestration technologies such as Docker and Kubernetes
- Experience working with cloud technologies such as AWS, alongside strong proficiency in Python, SQL and distributed data processing technologies
- Effective verbal and written communication skills, with the ability to adapt communication style for different audiences and stakeholder groups
- Additional skills that would be beneficial: RAG, semantic search and vector database experience, Knowledge graph and retrieval optimisation, MCP and tool-calling integration patterns for AI agents, LLM evaluation, observability and production monitoring
Hours
45Job Posting Closing Date:
19/10/2026Skills Required
- Experience designing, building, and supporting production machine learning and AI solutions
- Degree in a quantitative, engineering, or computer science discipline, or equivalent practical experience
- Strong software engineering experience, including cloud-native development, data engineering, and machine learning operations
- Technical leadership experience and ability to mentor others
- Experience delivering complex machine learning and AI solutions into production
- Strong object-oriented programming, API development, testing framework, source control, and modern development skills
- Experience developing, deploying, and supporting Generative AI and Agentic AI solutions
- Experience with orchestration frameworks, evaluation methodologies, and operational monitoring
- Expertise in MLOps, including model deployment, CI/CD, infrastructure as code, observability, model monitoring, and performance optimization
- Knowledge of Docker and Kubernetes
- Experience with AWS or other cloud technologies
- Strong proficiency in Python, SQL, and distributed data processing technologies
- Effective verbal and written communication skills for technical and non-technical audiences
- RAG, semantic search, vector databases, knowledge graphs, retrieval optimization, MCP, tool-calling integration, LLM evaluation, observability, and production monitoring
NatWest Group Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NatWest Group and has not been reviewed or approved by NatWest Group.
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Flexible Benefits — A flexible ValueAccount structure with pension and benefit funding allows tailoring of health, protection, lifestyle, and savings options, with unused amounts typically paid as cash. This flexibility supports personalisation of coverage, particularly in Great Britain where the framework is most detailed.
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Retirement Support — Employer-funded pension contributions are provided on top of salary in Great Britain, alongside automatic retirement enrollment and share/save programs. This creates structured long‑term wealth support as part of total reward.
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Parental & Family Support — UK policies outline extended maternity, adoption and equal partner leave on full pay with a phased return, plus paid neonatal care leave. These provisions are positioned as market‑leading and complement broader flexibility resources.
NatWest Group Insights
What We Do
We’re a business that understands when our customers and people succeed, our communities succeed, and our economy thrives. As part of our purpose, we’re looking at how we can drive change for our communities in enterprise, learning and climate. As one of the leading supporters of UK business, we’re prioritising enterprise as a force of change. We’re focusing on the people and communities who have traditionally faced the highest barriers to entry and figuring out ways to remove these. Learning is also key to our continued growth as a company in an ever changing and increasingly digital world. By setting a dynamic and leading learning culture, our people prosper, and our customers are given the tools to continue to improve their financial capability and confidence. One of the biggest challenges we all face in our future is climate change. That’s why we’ve put it right at the core of our purpose. We want to champion climate solutions with financing and entrepreneurial support, fully embed climate into our culture and decision making, and be climate positive by 2025. We’re committed to using our purpose to break down barriers, drive change and ultimately create a great place to work.
