Machine Learning Engineer, AVP

Posted 3 Days Ago
2 Locations
In-Office
Entry level
Fintech • Payments • Financial Services
The Role
Deploy, automate, monitor, and maintain machine learning models in production. Lead complex ML projects, optimize pipelines, develop monitoring frameworks, address model performance issues, and transform data science prototypes into scalable solutions. Collaborate with stakeholders and multidisciplinary Agile teams, coach colleagues, demonstrate AI tooling, and contribute to responsible AI automation, regression testing, and observability initiatives.
Summary Generated by Built In

Join us as a Machine Learning Engineer

  • We’re looking for someone to deploy, automate, maintain and monitor machine learning models and algorithms to make sure they work effectively in a production environment
  • Day-to-day, you’ll collaborate with colleagues to design and develop state-of-the-art machine learning products which power our group for our customers
  • This is your opportunity to turn your interests into a diverse and rewarding career, as you solve new problems and create smarter solutions in a non-stop innovation environment
  • We're offering this role at associate vice president level
What you’ll do

As a Machine Learning Engineer, you’ll lead the planning and design of complex projects. Your daily responsibilities will see you codifying and automating machine learning model production, including pipeline optimisation, tuning and fault finding, as well as transforming data science prototypes and applying appropriate machine learning algorithms and tools.

We’ll need you to deploy and maintain adopted end-to-end solutions, including building metrics to improve system performance and identifying and resolving differences in data distribution which affect model performance.

In addition, you’ll be responsible for:

  • Understanding the needs of our business stakeholders, and how machine learning solutions meet those needs to support the achievement of our business strategy
  • Working with colleagues to produce machine learning models, including pipeline designs, development, testing and deployment to carry on the intent and knowledge into production
  • Creating frameworks to make sure the monitoring of machine learning models within the production environment is robust
  • Delivering models that adhere to expected quality and performance while understanding and addressing any shortfalls, for example through retraining
  • Leading and working in an Agile way within multi-disciplinary data and the analytics teams to achieve agreed project and Scrum outcomes
The skills you’ll need

To be successful in this role, you’ll have an academic background in a STEM discipline, like Mathematics, Physics, Engineering or Computer Science. You’ll need experience with machine learning on large datasets and an understanding of machine learning approaches and algorithms.

Alongside this, you’ll have experience of building, testing, supporting and deploying machine learning models into a production environment, using modern CI/CD tools, like TeamCity and CodeDeploy. You’ll also have good communication skills to engage with a wide range of stakeholders.

Furthermore, you’ll need:

  • Experience of coaching others
  • Knowledge of data science and machine learning
  • Experience of python programming with hands on experience with  ai/ml both traditional ML and well versed GenAI and Agentic AI applications
  • Responsible for conducting  demos of AI tooling and  would be contributing towards automation initiatives in responsible ai space things like QA regression suite and observability aspects
  • Financial services knowledge and the ability to identify wider business impacts, risks and opportunities to make connections across key outputs and processes

Hours

45

Job Posting Closing Date:

17/09/2026

Skills Required

  • Academic background in a STEM discipline such as Mathematics, Physics, Engineering, or Computer Science
  • Experience with machine learning on large datasets and knowledge of machine learning approaches and algorithms
  • Experience building, testing, supporting, and deploying machine learning models in production
  • Experience using modern CI/CD tools such as TeamCity and CodeDeploy
  • Good communication skills for engaging with a wide range of stakeholders
  • Experience coaching others
  • Knowledge of data science and machine learning
  • Python programming experience with hands-on traditional machine learning, Generative AI, and Agentic AI applications
  • Experience conducting demonstrations of AI tooling
  • Ability to contribute to responsible AI automation initiatives, including QA regression suites and observability
  • Financial services knowledge and ability to identify business impacts, risks, and opportunities

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.

  • 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.
  • 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.
  • 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

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The Company
HQ: Edinburgh
40,000 Employees
Year Founded: 1970

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.

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