1 to 25 of 71 Semantic Search Jobs in the UK excluding London

production—in a modern, AWS-based environment. You'll be part of an engineering and data team tackling projects like LLM development, intelligent automation, semantic search, and recommendation systems. There's real scope here to influence how MLOps and model deployment are done, and you'll be working … Implementing MLOps practices: versioning, experiment tracking, CI/CD, monitoring Optimising for performance, scalability and cost in the cloud Integrating vector databases, embeddings and semantic search Collaborating with engineering, data and product teams What you'll need: Strong Python and experience with TensorFlow, PyTorch or scikit-learn Hands ...

looking for a founding AI engineer to help design and build the core of its AI platform. AI is central to the product, powering semantic search, intelligent matching, assistants, and personalised experiences across web and mobile. You'll work closely with the founders to design and deliver creative … features that genuinely change how people discover and book services. What you'll be doing Architect and build AI-driven backend services powering semantic search, natural language interfaces, recommendations and assistants Integrate and deploy LLM APIs (OpenAI, Anthropic, Cohere) and open-source AI frameworks (LangChain, LlamaIndex, LangGraph) into ...

Knowledge Systems Implementation of Retrieval-Augmented Generation (RAG) pipelines Experience with vector databases such as CockroachDB, Pinecone, or similar Embedding model optimisation and semantic search Context window management and chunking strategies MCP & Tool Integration Model Context Protocol (MCP) implementation LLM tool calling and API design AWS Lambda integration ...

Design RAG systems: chunking strategies, document schemas, metadata, hybrid/dense retrieval, re-ranking, and grounding. Manage vector/keyword indexes (e.g., Azure AI Search, pgvector, Pinecone/Weaviate). Develop and deploy advanced NLP, information retrieval, and recommendation systems that enhance Chambers and Partners’ research and product offerings … including document understanding, automatic summarisation, topic modelling, semantic search, entity recognition, and relationship extraction. Design and implement intelligent tagging and metadata enrichment frameworks to categorize and organize legal and market data, improving search, discoverability, and insight accuracy. LLM & Machine Learning Application Engineering Design, build, and maintain traditional ...

Research, MLOps, Data Pipelines, Prompt Engineering, Model Fine-Tuning, Cloud Computing, AWS, Azure, Google Cloud, AI Infrastructure, Transformers, Reinforcement Learning, Vector Databases, Pinecone, Weaviate, Semantic Search, API Development, AI Deployment, Model Serving, AI Automation, Early Stage Startup, AI Startups, Tech Startup, Machine Intelligence, Applied AI, AI Applications ...

team. The ideal candidate will bring deep expertise in building and deploying AI/ML solutions, particularly in areas like NLP, document understanding, and search/retrieval systems. You will work in a dynamic environment, collaborating across teams to design scalable AI-driven solutions and bring them into production. … optimization. Work with LLM architectures and frameworks (OpenAI, Anthropic, LangChain, LlamaIndex) to build scalable and intelligent applications. Utilize vector databases (e.g., FAISS, Azure AI Search) and embedding models for semantic search and retrieval tasks. Execute and iterate on PoCs rapidly to evaluate feasibility and solution effectiveness. Collaborate ...

DTAC) Frontend React TypeScript Tailwind Component-driven development AI/RAG/Knowledge Pipelines LLM-powered features RAG pipelines (embedding models, chunking strategies, semantic search) Document extraction/processing (PDF parsing, OCR, agentic pipelines) Clinical content ingestion workflows Evaluation frameworks for safe medical AI responses … support. Nice to Have Experience coding in TypeScript, JavaScript, Python, or similar. Understanding of cloud systems (AWS preferred). Familiarity with LLMs, embeddings, or semantic search. A portfolio of side projects, tools, or apps you've built (clinical or not). Experience in a startup or fast-moving environment. ...

ship backend systems from day one — scalable architectures, APIs, and AI-powered tools. Build and deploy LLM-based applications using frameworks such as LangChain, semantic search, RAG, and fine-tuning (SFT/RL). Develop backend logic and data pipelines to power intelligent automation and real-time processing. ...

agile environment. Excellent communication and collaboration skills. Nice to Have Experience with workflow orchestration tools (e.g., Temporal, Airflow). Familiarity with vector databases or semantic search tools. Experience building internal tooling or developer productivity enhancements using AI. Exposure to customer support AI tools or RAG (retrieval augmented generation ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...

areas: Computer Vision: Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product ...