Senior AI Engineer

Overview

In this role you will shape Multiverse into an AI-first company by building end-to-end agent systems that integrate with the current product. You will work across engineering to redesign how the company operates, improve learner outcomes, and set the standard for AI-driven product development. You will balance context management, tooling, and evaluation to deliver scalable, trusted AI solutions. This is a chance to influence industry best practices and help Megaverse scale its AI-enabled education platform.

Pay / Benefits

• 27 days holiday plus 5 wellbeing days
• private medical insurance with Bupa
• life insurance
• gym membership & wellness resources
• Wellhub and Spill mental health support
• hybrid work (London) with three days in office (location varies)

Responsibilities

• Own and deliver complete agent systems—architecture, implementation, evaluation, and production operation
• Design context and retrieval strategies for context windows, including memory, summarisation, and chunking
• Build evaluation frameworks with automated pipelines and human-in-the-loop processes
• Design tool integrations (APIs, data contracts, error handling) for reliable agent interactions
• Influence technical direction through architecture and decision-making
• Raise the bar through code review and pair programming to raise production-quality AI engineering
• Use Claude Code as the primary development workflow and mentor others in AI-assisted development

Key requirements

• Shipped AI systems at scale serving real users
• Deep understanding of context management, retrieval, chunking, and summarisation
• Experience with model selection, routing, and cost engineering
• Proficient in tool use and agent augmentation with external systems
• Experience building evaluation frameworks for AI outputs
• Full-stack delivery capabilities across LLM integration, backend services, data pipelines, and some frontend
• Strong communication skills for documenting designs and giving feedback

• Strong communication
• Product thinking and user-centric problem solving
• Collaborative and cross-functional teamwork
• Context management and retrieval strategies
• Chunking, conversation memory, summarisation
• Model selection, routing, and cost engineering (token economics, caching, batching)

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