Machine Learning Engineer, Platform

<p><strong>Machine Learning Engineer</strong><strong><br></strong>London, UK</p> <p><strong>About the role</strong></p> <p>Applied Intelligence Systems (AIS) is part of the Scale Generative AI Platform (SGP), focused on pushing the frontier of what agentic applications can do across diverse enterprise and government use cases. We build the infrastructure and tooling that power agentic AI in production, paired with applied ML research, design, and evaluation to ensure these systems perform reliably at the scale our customers demand. AIS spans multiple workstreams — agent evaluation and oversight, orchestration and tool-use infrastructure, model and systems optimization, and applied research on new agent capabilities. This role owns the context and memory capabilities within AIS, including their correctness, performance, and evaluation.&nbsp;</p> <p>We are looking for a Machine Learning Engineer who can own hard technical problems end to end — from research and prototyping through to production deployment — working across knowledge bases, vector stores, RAG pipelines, and context engines to power agents that deliver real impact for enterprise customers.&nbsp;</p> <p><strong>What you'll do</strong></p> <ul> <li>Own large areas of the platform end to end, from design through to production deployment.</li> <li>Work on knowledge representation systems, including ontologies and knowledge graphs, to support structured reasoning over enterprise data.</li> <li>Design and implement RAG pipelines, including chunking, embedding, indexing, retrieval, and reranking.</li> <li>Build and maintain integrations between retrieval and ML components and diverse enterprise data sources, vector databases, APIs, and services.</li> <li>Develop context retrieval systems that balance recall, precision, latency, and cost.</li> <li>Build evaluation frameworks, datasets, and metrics to measure retrieval quality, context relevance, and end to end agent performance.</li> <li>Build reliable backend s