Applied AI Engineer

Applied AI Engineer Multiverse is the upskilling platform for AI and Tech adoption. We have partnered with 1,500+ companies to deliver a new kind of learning that's transforming today’s workforce. Our upskilling apprenticeships are designed for people of any age and career stage to build critical AI, data, and tech skills. Our learners have driven $2bn+ ROI for their employers, using the skills they’ve learned to improve productivity and measurable performance. In June 2022, we announced a $220 million Series D funding round co-led by StepStone Group, Lightspeed Venture Partners and General Catalyst. With a post-money valuation of $1.7bn, the round makes us the UK’s first EdTech unicorn. But we aren’t stopping there. With a strong operational footprint and 800+ employees, we have ambitious plans to continue scaling. We’re building a world where tech skills unlock people’s potential and output. Join Multiverse and power our mission to equip the workforce to win in the AI era. THE JOB DESCRIPTION At Multiverse, we believe technology should empower everyone to achieve their potential. As a Senior member of the Applied AI team, you will sit at the intersection of data science and software engineering. You won’t just build models in isolation; you will transform cutting-edge AI research into scalable, real-world products that make learning and development smarter and more personalised for thousands of users. You will play a pivotal role in steering our AI roadmap, guiding collaborative efforts across product and engineering to shape the future of work and education. KEY RESPONSIBILITIES - Design & Deliver AI/ML solutions: Translate complex stakeholder queries and business hypotheses into actionable experiments and AI/ML model requirements. Partner with Product and Design to deliver features that align with Multiverse’s mission. - Architect LLM & Agentic Workflows: Design and integrate LLM-powered solutions (e.g., GPT, Claude, Gemini) for content generation and personalized learning. Build out our Knowledge Graph capability to underpin agentic workflows and semantic search. - Own the End-to-End Lifecycle: Take full ownership of the journey from data lineage and preprocessing through to experimentation, deployment, evaluation and continuous iteration. Ensure all models adhere to software engineering best practices for production-grade reliability. - Experimentation Rigour & Quality: Proactively monitor and refine models to optimise effectiveness while minimising sampling/analytical biases and operational challenges. Build robust evaluation frameworks to measure accuracy, safety, and inclusivity. - Lead in MLOps & Infrastructure: Build and maintain scalable pipelines for model training and deployment using AWS and modern MLOps practices. Leverage AI-assisted tools like Cursor and Gemini to accelerate development velocity. - Strategic Influence & Mentorship: Bridge the gap between technical concepts and business objectives by communicating actionable insights to stakeholders. Share your expertise to make AI approachable, helping colleagues across Multiverse see how it can enhance their work. ABOUT YOU Technical Mastery - Experience: 5+ years of Data Science, Machine Learning, or AI Engineering experience, with a proven track record of leading complex AI/ML projects from concept to production. - ML & LLM Stack: Proficient in Python and its ecosystem (e.g. NumPy, Pandas, Scikit-Learn, PyTorch). Deep experience with LLM orchestration (e.g. Langchain) and prompt engineering. - Data Engineering: Advanced knowledge of SQL and experience working with both structured and unstructured data - Software Rigor: Strong proficiency in building APIs and microservices. Comfortable with GitHub, CI/CD, observability & evaluation practices, and Infrastructure as Code (e.g., Terraform). - Cloud Native: Practical experience deploying and monitoring AI solutions within AWS, Azure or similar cloud environments. Mindset & Approach - Ana ... (truncated, view full listing at source)