4 of 4 Remote/Hybrid Semantic Search Jobs in the North of England

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 ...

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 ...

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 ...