Off-Cycle AI Intern (Trading), 2026, London

Overview

Off-Cycle AI Intern (Trading), 2026, London

We are seeking a curious, driven, and innovative intern to join a fundamental investment team and help design, develop, and implement AI-powered tools and agent-based workflows that improve research processes, information capture, and decision-making. As an intern, you will gain hands-on experience working on real-world projects in a collaborative environment while contributing to internal technology that supports investment professionals.

Responsibilities

• Design, develop, test, and maintain AI-powered tools and agent-based systems that support research workflows for fundamental investment professionals.
• Build internal applications and workflow tooling to improve knowledge discovery, information capture, and decision support for the investment process.
• Develop data and research infrastructure for acquiring, organizing, normalizing, and surfacing structured and unstructured information used by the team.
• Analyze real-world datasets and implement AI or machine learning components to improve retrieval, ranking, extraction, summarization, and workflow efficiency.
• Write clean, maintainable, and well-documented code; troubleshoot, debug, and improve existing systems and processes using strong software engineering practices.
• Optimize systems for scalability, reliability, and performance while staying current with emerging technologies and engineering approaches relevant to AI tooling.

Qualifications, Skills, and Requirements

• Graduating between December 2025 and June 2026 from a degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or related fields.
• 3–6 month internship duration (to be discussed with the selected candidate).
• Locations: London, Dubai, or Paris.
• Strong programming skills in Python or similar languages.
• Experience with Claude Code.
• Strong foundation in data structures and algorithms.
• Experience building software tools, internal applications, data workflows, or research-enablement infrastructure.
• Comfortable working with data using tools such as SQL, Pandas, Spark, or similar technologies.
• Familiarity with machine learning frameworks or applied AI tooling such as PyTorch, TensorFlow, scikit-learn, or related libraries is preferred.
• Experience examining real-world datasets and a solid understanding of statistical and machine learning techniques.
• Strong problem-solving skills, attention to detail, and the ability to collaborate effectively between technical and non-technical stakeholders in a fast-paced environment.
• Genuine interest in financial markets and the investment process.