About the role
We are looking for an experienced Applied AI / ML Engineer to join a high-performing technology team building production-grade AI and machine learning solutions for complex business and quantitative workflows. This role is ideal for someone who combines strong software engineering fundamentals with hands-on experience in machine learning, MLOps and quantitative applications. You will work closely with experienced engineers, researchers and business stakeholders to turn AI/ML concepts into scalable, reliable systems used in real-world production environments.
Responsibilities
•Design, develop and deploy production-grade machine learning and AI solutions addressing 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 new AI/ML approaches and technologies.
•Build robust data and machine learning pipelines, from data preparation and feature engineering through to model deployment and monitoring.
•Develop, test and maintain high-quality, scalable and production-ready software.
•Apply MLOps practices including model versioning, monitoring, deployment and lifecycle management.
•Work extensively with AWS technologies, including services such as SageMaker, Lambda and S3.
•Contribute to and create reusable libraries, frameworks and engineering standards that enable 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 closely related field.
•Master's or PhD in Computer Science, Machine Learning, Mathematics, Statistics, Physics, Engineering, Quantitative Finance or a related discipline — or equivalent industry experience.
•Strong experience building and deploying machine learning models in production, preferably within AWS environments.
•Strong programming skills in Python and experience with another modern language such as C++ or Java.
•Solid understanding of data preprocessing, feature engineering, model evaluation and ML lifecycle management.
•Practical experience with MLOps, including model monitoring, versioning, deployment and reproducibility.
•Hands-on experience with AWS services such as SageMaker, Lambda and S3.
•Strong software engineering mindset, with an emphasis on code quality, testing, scalability and maintainability.
•Ability to take ownership of 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 is highly desirable.
•Experience working with equities, fixed income, multi-asset strategies or other quantitative investment domains is a strong advantage.
Conditions
•B2B Contract secured until 31 March 2027, with an intention to extend for up to 4 years in total.
•Opportunity to work on high-impact AI and machine learning problems where research meets production engineering.
•Opportunity to influence how AI/ML systems are designed, developed and deployed, while working in an environment that values technical excellence, experimentation, collaboration and measurable business impact.
