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# Machine Learning Engineer, Platform

- **Company:** Ais
- **Location:** London, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, Application Programming Interfaces (APIs), Artificial Intelligence, Automated Storage and Retrieval Systems, Encodings, Graph Database, Python (Programming Language), Machine Learning, Search Technologies, Software Deployment, Enterprise Data Management, Large Language Models, Indexer, Backend, Knowledge Representation, Information Technology, Data Pipelines
- **Published:** September 27, 2026
- **Apply:** https://www.apply4u.co.uk/jobs/machine-learning-engineer-platform/48667831

## About the Role

infrastructure teams to shape the direction of the platform. What we look for 5+ years of experience building and deploying machine learning or AI systems for real-world, production use cases. Strong engineering fundamentals, supported by a Master's or PhD degree in Computer Science, Machine Learning, AI, or equivalent practical experience.. A deep, hands-on understanding of retrieval systems, RAG, embeddings, vector indexing, knowledge representation, and semantic search. Proven proficiency in Python, including writing production-quality, testable, and maintainable code. The ability to operate in ambiguous problem spaces, balancing research-driven approaches with pragmatic product constraints. Strong communication skills and comfort working in customer-facing or cross-functional environments. Experience scaling products at hyper growth startups PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and

## Description

impact for enterprise customers. What you'll do Own large areas of the platform end to end, from design through to production deployment. Work on knowledge representation systems, including ontologies and knowledge graphs, to support structured reasoning over enterprise data. Design and implement RAG pipelines, including chunking, embedding, indexing, retrieval, and reranking. Build and maintain integrations between retrieval and ML components and diverse enterprise data sources, vector databases, APIs, and services. Develop context retrieval systems that balance recall, precision, latency, and cost. Build evaluation frameworks, datasets, and metrics to measure retrieval quality, context relevance, and end to end agent performance. Build reliable backend services and data pipelines that support ML and LLM components in production. Deliver experiments and new capabilities quickly, maintaining high quality and tight feedback loops with customers. Collaborate across product, ML, and


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