AI Developer

Overview

• Design and deploy platform features, data pipelines, automations, and internal tools that improve Allocator's data aggregation, analytics, and reporting workflows.
• Use AI coding assistants as core daily tools; we value problem-solving ability and execution speed over memorising syntax.
• Build agentic workflows and AI-driven features into our platform and internal tooling, applying LLMs and orchestration frameworks where they deliver real leverage.
• Analyse workflows across data collection, fund reporting, and client delivery, identifying improvement opportunities and implementing practical solutions.
• Move from concept to prototype to production efficiently. Launch MVPs, track performance, and iterate based on client and internal feedback.
• Collaborate with Product, Data, and Client Success teams to understand requirements, demonstrate solutions, and support adoption.
• Establish performance baselines, implement changes, and document measured impact for every feature shipped.
• Lead discovery sessions with Product and Data teams, documenting success criteria, technical specifications, and trade-offs clearly.
• Develop release notes, setup guides, and onboarding materials accessible to non-technical colleagues and clients.

• Bachelor's degree (Master's preferred) in Computer Science, Applied Mathematics, Engineering, Statistics, Physics, or a related quantitative discipline
• First-Class or Upper Second-Class Honours (2:1 or above) from a UK or internationally recognised university, or equivalent (e.g. GPA 3.5+/4.0)
• Strong written and verbal communication, capable of producing technical specifications, presenting demonstrations, and translating between technical and business contexts
• Analytical mindset with the ability to decompose complex, ambiguous challenges into actionable steps
• Programming foundation in Python, SQL, or JavaScript (academic projects, internships, or capstone work)
• Daily working fluency with AI development tools (ChatGPT, Claude, Git Hub Copilot, Cursor), used as core tools rather than occasional aids
• Bias for action; prefers experimentation and data-driven iteration over extended planning
• Desirable:
  Hands-on experience building with LLMs, prompt engineering, or AI agent frameworks in a production environment
• Background in data analysis, process optimisation, or computational modelling
• Internship or work experience in fintech, asset management, consulting, or technology
• Familiarity with or interest in alternative investments, fund data, or institutional finance