Machine Learning Engineer

Job Description

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 What you ll do 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. You ll also maintain knowledge of data science and machine learning. 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 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 using programming and scripting languages, such as Python and Bash An understanding of how to synthesise, translate and visualise data and insights for key stakeholders Understanding of the capabilities and experience with Large Language Models and their APIs Ability to read and understand a large documentation base, as well as contribute to it Desire to understand the business requirements and limitations, and expertise to make relevant suggestions Strong software engineering, systems architecture, and unit testing capabilities Experience with AWS/other cloud providers Experience with GitLab CI/CD pipelines for automated testing and deployments Experience using pipeline tools such as Apache Airflow, Amazon SageMaker or similar Familiarity with SQL Experience with MLOps and model monitoring tools such as Splunk, Comet ML, etc An understanding of how to present the data and insights for key stakeholders Financial services knowledge and the ability to identify wider business impacts, risks and opportunities to make connections across key outputs and processes