About the Team
The technology team builds production-grade AI and machine learning solutions for complex business and quantitative workflows. It develops scalable, reliable systems used in real-world production environments.
Responsibilities
•Design, develop, and deploy production-grade machine learning and AI solutions for complex business and quantitative problems
•Collaborate with engineers, researchers, and domain experts to advance production ML systems and applications
•Research, prototype, experiment with, and evaluate AI/ML approaches and technologies
•Build data and machine learning pipelines, from data preparation and feature engineering through model deployment and monitoring
•Develop, test, and maintain scalable, production-ready software
•Apply MLOps practices, including model versioning, monitoring, deployment, and lifecycle management
•Work with AWS technologies, including SageMaker, Lambda, and S3
•Create reusable libraries, frameworks, and engineering standards for reliable and testable ML systems
•Take technical ownership of projects and demonstrate leadership across teams
•Represent the organisation at technology and AI/ML conferences, industry events, and open-source communities
Requirements
•5+ years of professional experience in software engineering, machine learning, AI, quantitative technology, or a related field
•Master's or PhD in Computer Science, Machine Learning, Mathematics, Statistics, Physics, Engineering, Quantitative Finance, or equivalent industry experience
•Experience building and deploying machine learning models in production, preferably in AWS environments
•Strong Python skills and experience with another modern language such as C++ or Java
•Understanding of data preprocessing, feature engineering, model evaluation, and ML lifecycle management
•Practical MLOps experience, including model monitoring, versioning, deployment, and reproducibility
•Hands-on experience with AWS services such as SageMaker, Lambda, and S3
•Strong software engineering mindset focused on code quality, testing, scalability, and maintainability
•Ability to own technically complex projects and work effectively across teams
Nice to Have
•Experience developing software for quantitative investment, trading, financial modelling, or other data-intensive workflows
•Experience with equities, fixed income, multi-asset strategies, or other quantitative investment domains
Conditions
•B2B contract secured until 31 March 2027, with an intention to extend for up to 4 years in total
•Hybrid work model with 3 days in the office
