9 of 9 Remote/Hybrid Semantic Search Jobs in the UK excluding London

capability without long-term reliance on external suppliers. Key Responsibilities Architectural Leadership & Integration Design and implement enterprise-grade AI platforms - including semantic search, Retrieval-Augmented Generation (RAG), and broader generative AI/agentic capabilities - integrating cleanly with the department's complex Legacy and multi-cloud environments. Responsible … DDaT competencies, given the scale of this programme, hands-on depth across the modern AI engineering stack is essential, not just RAG in isolation: Semantic search, vector/embedding infrastructure, and retrieval architecture design LLM orchestration and agentic frameworks (eg LangChain/LlamaIndex-style tooling, multi-agent patterns ...

investigation and prototype development rather than production engineering. The successful candidate will work with ambiguous requirements, explore datasets, and design potential approaches to classification, semantic search, matching and comparison of text-heavy data. The emphasis is on understanding the data, testing hypotheses, and developing proof-of-concept solutions … Data Science & Modelling Explore structured and unstructured datasets to identify patterns and opportunities Build proof-of-concept ML and statistical models Apply NLP and semantic techniques to classify, compare and relate text data Test and evaluate different modelling approaches NLP & Text Analytics Work with text data including titles, summaries ...

Databricks or similar technologies Experience architecting scalable AI and ML solutions across serverless, containerised or GPU enabled environments Experience with LLMs, prompt engineering, embeddings, semantic search and RAG patterns Exposure to vector databases and agent frameworks such as LangChain, LangGraph or similar technologies Understanding of MLOps, LLMOps ...

Databricks or similar technologies Experience architecting scalable AI and ML solutions across serverless, containerised or GPU enabled environments Experience with LLMs, prompt engineering, embeddings, semantic search and RAG patterns Exposure to vector databases and agent frameworks such as LangChain, LangGraph or similar technologies Understanding of MLOps, LLMOps ...

tested, deployed and version-controlled Proactively monitoring pipeline health, optimising performance, and managing infrastructure costs to ensure efficient scaling Applying strong data modelling, semantic consistency and metadata discipline so data is discoverable and usable by both humans and AI tools Partnering with function-specific AI engineers and analysts … with Cloud Build and GitHub for version control and CI/CD A working understanding of how to keep data AI-ready: strong modelling, semantic consistency, and metadata discipline that both humans and AI tools can rely on Comfortable working across the full data landscape that a growing ...

GenAI architecture strategy and roadmap. Build scalable AI/ML and Agentic AI platforms spanning LLMs, retrieval-augmented generation (RAG), embeddings and semantic search. Lead on deployment, governance and integration patterns, advise clients on platform capability and implementation strategy, and partner with engineering, data, DevOps and business teams ...

GenAI architecture strategy and roadmap. Build scalable AI/ML and Agentic AI platforms spanning LLMs, retrieval-augmented generation (RAG), embeddings and semantic search. Lead on deployment, governance and integration patterns, advise clients on platform capability and implementation strategy, and partner with engineering, data, DevOps and business teams ...

document parsing, OCR and image-based data extraction . Experience with LLM orchestration frameworks and prompt design . Experience with vector databases, embeddings and semantic search . Experience implementing retrieval techniques including chunking, indexing, metadata tagging, filtering and reranking . Experience working with SQL and/or NoSQL ...

prompt engineering, or RAG pipeline construction * Familiarity with REST-based AI APIs (OpenAI, Anthropic, Google Vertex AI, or equivalent). * Basic understanding of embeddings, semantic search, and token-based context management. * Comfortable reading Python code or notebooks (most AI libraries are Python-first); ability to translate patterns into ...