Applied AI / ML engineer in quantitative finance

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.

• 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.

• 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.
• Будет плюсом: 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

• B2B Contract secured until 31 March 2027, with an intention to extend for up to 4 years in total.
• This is an opportunity to work on high-impact AI and machine learning problems where research meets production engineering.
• You will have the 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.