Machine Learning / AI Engineer

• Build & scale agentic AI systems: Own the design, development and deployment of production-grade agentic systems that power adaptive learning experiences - from multi-step reasoning pipelines to autonomous feedback loops that respond to learner behaviour in real time.
• LLMs & RAG architectures: Architect and integrate LLM-powered features using retrieval-augmented generation (RAG), prompt engineering strategies, and evaluation pipelines. Lead application of these to high-impact use cases such as mistake analysis, content generation, and personalised learning paths.
• Agentic frameworks: Lead the design of multi-agent workflows using frameworks such as LangChain and LangGraph. Define agent orchestration patterns, tool use, memory, and state management strategies for production environments, and establish best practices across the team.
• Full ML lifecycle ownership: Collaborate with Data Scientists and Senior ML Engineers to move models from experimentation to production, including feature engineering, training pipelines, online inference, and monitoring. Take ownership of reliability and quality end to end.
• Platform & tooling development: Drive improvements to our ML infrastructure and experiment orchestration tools (e.g. MLFlow, Airflow, SageMaker, Kubernetes), and help make AI development faster and safer across the team.
• Cross-functional collaboration: Work closely with Data Engineers, Product Managers, Designers, and other engineers to embed intelligence into our products, improve experimentation velocity, and drive measurable learning outcomes.
• Research & innovation: Lead research spikes on emerging AI/ML technologies - from graph-based knowledge representations to advanced RAG patterns, fine-tuning strategies, and agentic evaluation frameworks. Shape our evolving AI strategy and contribute to the wider engineering community., * Centrally located offices with free breakfast, snacks, and fresh fruit
• 2 free lunches per week from a wide selection of restaurants
• Great Private Health Insurance scheme
• Personal training budget to keep growing
• Flexible working hours and a hybrid model of working
• Enhanced maternity and paternity leave
• Frequent social activities: team lunches, Thursday socials, quarterly events

Our platform is for everyone, and so is our workplace. We embrace our differences - cultural, racial, religious, or otherwise - and believe every voice matters.

• Strong foundations in machine learning, applied AI, and software engineering. You write clean, maintainable Python code, are comfortable designing ML pipelines, and can work across APIs and microservices at scale.
• Proven experience building and deploying agentic AI systems using LangChain and/or LangGraph - including agent orchestration, tool use, and multi-step reasoning pipelines in production environments.
• Deep practical knowledge of LLMs (e.g. OpenAI, Anthropic, HuggingFace) and hands-on experience with prompt engineering, vector stores, and RAG architectures. You know how to evaluate and iterate on these systems rigorously.
• Proficiency in building data and training pipelines using tools like SQL, Airflow, or AWS services (S3, SageMaker, Lambda).
• Proven track record deploying ML or AI systems to production, especially around NLP, personalisation, or recommendation. A/B testing and impact evaluation experience is a strong plus.
• Graph-based data structures or graph databases (e.g. Neo4j, NetworkX) is a strong plus.
• Excellent communication skills and ability to work collaboratively in a diverse, cross-functional team. You take ownership, iterate fast, and make others around you better.
• Strong analytical thinking and genuine curiosity about the user experience and pedagogical impact of AI solutions.
• Experience in EdTech, adaptive learning, or consumer personalisation is a big plus, not a requirement.