Machine Learning Engineer

Join us as a Machine Learning Engineer

• In this key role, you’ll deploy, automate, maintain, and monitor machine learning models, and 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 business and deliver the best outcomes for our customers
• This is an opportunity to actively participate in the data and analytics community and identify and deliver opportunities to support our strategic direction through a better use of data
• You’ll work from home some of the time, but you’ll also spend a significant amount of time working from the Edinburgh or Manchester office 

What you'll do

Your daily responsibilities will include deploying and maintaining adopted end-to-end solutions, improving system performance, and identifying changes which affect model performance. You’ll also be responsible for pipeline optimisation, tuning and fault finding, automating, improving, and transforming data science prototypes into productionised models.

You’ll investigate model performance issues with Large Language Models (LLMs), and you’ll help to identify root causes and recommend a course of action to resolve issues. You’ll also support Ask Archie+, our chatbot which helps colleagues with their queries regarding HR and technology. In doing so, you’ll make recommendations for document updates to maximise the chatbot’s document retrieval performance, using your strong command of language, semantics, and prompt engineering techniques.

You’ll also be responsible for:

• Using your experience and knowledge of LLMs to optimise prompt engineering within the model
• 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 the 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
• Maintaining your knowledge of software engineering, data science, and machine learning
• Collaborating with multi-functional teams in an Agile way to achieve shared goals and using your network within data and analytics to benefit from model frameworks and best practices

The skills you'll need

We’re looking for someone with experience of building, testing, supporting, and deploying machine learning models into a production environment. You’ll need systematic debugging skills, the ability to navigate a large well-structured codebase, and a willingness to work with complex systems. We’ll also expect you to demonstrate good communication skills, able to engage with stakeholders and build strong relationships across multi-disciplinary teams.

Along with financial services knowledge and the ability to identify wider business impacts, risks, and opportunities to make connections across key outputs and processes, you’ll have the desire to understand the business requirements and limitations, and expertise to make relevant suggestions. You’ll have an understanding of how to present data and insights for key stakeholders and experience using programming and scripting languages such as Python and Bash.

Additionally, you’ll need:

• An understanding of the capabilities and experience with LLMs and their APIs, and the ability to read and understand a large documentation base, as well as contribute to it
• Strong software engineering, systems architecture, and unit testing capabilities and experience using pipeline tools such as Apache Airflow, Amazon SageMaker, or similar
• Familiarity with SQL and experience with AWS or other cloud providers
• Experience with GitLab CI/CD pipelines for automated testing and deployments
• Experience with MLOps and model monitoring tools such as Splunk and Comet ML

Hours

Job Posting Closing Date: