7 of 7 Permanent Semantic Search Jobs in the North of England

vector databases and traditional data platforms. Model complex business domains to improve retrieval quality and contextual understanding. Design and optimise hybrid retrieval strategies using semantic search, keyword search, embeddings and knowledge graphs. Evaluate, integrate and extend best-of-breed tooling rather than building infrastructure unnecessarily. Measure, monitor … scalable knowledge systems for AI-powered applications. Strong understanding of domain modelling, ontology design and information architecture. Hands-on experience with hybrid retrieval techniques, semantic search, embeddings and vector databases. Experience building reliable data pipelines and integrating multiple data sources. Excellent judgement in selecting the right technologies ...

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 ...

role: - Design AI and GenAI architecture strategies and roadmaps - Build scalable AI/ML and Agentic AI platforms - Work across LLMs, RAG, embeddings and semantic search - Support AI deployment, governance and integration patterns - Collaborate with engineering, data, DevOps and business teams - Advise clients on AI platform capability ...

experience building solutions using LLMs, machine learning or applied AI techniques to solve practical business problems. Experience with prompt engineering, retrieval augmented generation, semantic search, AI workflow orchestration or agent-based approaches. Experience working with business or domain data to ground, evaluate and improve AI outputs. Ability … subject matter experts to shape ideas into deliverable software. Desired Skills Experience working with Azure PaaS services, Azure AI Foundry, Azure OpenAI, Azure AI Search or Azure Machine Learning. Experience with .NET, SQL Server and cloud-native application development. Knowledge of Semantic Kernel or similar AI orchestration frameworks. ...

mitigating hallucinations and output errors. Deploy and maintain RAG architecture and LLM pipelines within cloud environments. Optimise data-chunking and text embeddings for optimal semantic search across a large, specialist corpus. Track the competitive landscape and stay current with technological advancements in GenAI. Monitor and optimise agent ...