26 to 50 of 117 Semantic Search Jobs in England

tools and data capabilities that allow AI agents to securely access, understand and use data. A key initial focus will be creating reusable search, retrieval and agentic capabilities that enable sophisticated internal research and automation using corporate data. You will also contribute to the wider development of GenAI capabilities … Help develop reusable tools that enable AI agents to securely discover, retrieve and use corporate data, including data held within our corporate data warehouse. Search & Retrieval: Develop semantic, vector and hybrid retrieval capabilities across structured and unstructured enterprise data. MCP & Agent Tooling: Build MCP servers, APIs and reusable ...

discovery experiences — from Scopus AI and LeapSpace to ClinicalKey AI, PharmaPendium, and next-generation life sciences platforms. These products leverage retrieval-augmented generation (RAG), semantic search, and generative AI to make knowledge more discoverable, connected, and actionable across disciplines. The Search & AI Evaluation team sits within … Platform Data Science organization and is responsible for advancing enterprise-scale search, retrieval, and evaluation capabilities across Elsevier's global products. About the role We are looking for a Senior Data Analyst to lead analytics and evaluation efforts for search and retrieval systems. You will own key analytical ...

someone who can combine technical depth with architecture-level thinking. You should be confident designing AI platforms, RAG systems, LLM applications, multi-agent workflows, semantic retrieval solutions, and MLOps pipelines for regulated or enterprise-scale environments. Key Responsibilities Design enterprise AI architectures using GenAI, LLMs, RAG, agentic AI, vector … databases, and cloud-native platforms. Define scalable architecture patterns for production AI applications. Lead solution design across semantic search, document intelligence, AI assistants, knowledge retrieval, workflow automation, and decision support systems. Build architecture approaches for retrieval orchestration, embeddings, reranking, grounding validation, observability, and evaluation frameworks. Work with engineering ...

services behind a SaaS platform. You will be responsible for turning documents and records into structured information, connecting related data, and supporting search and matching with clear supporting evidence. This role is open to both senior and mid-level engineers with relevant practical experience. Key Responsibilities Develop RDF models … extracted information into the graph, including incremental updates, duplicate handling, and pre-publication checks. Ensure corrections and approved deletions reach the graph and search indexes, respecting tenant boundaries and access permissions. Develop information extraction services for documents, messages, and transcripts using FastAPI and schema validation, ensuring source location ...

Pinecone (essential for RAG architectures). Data Orchestration: Familiarity with Airflow, Prefect, or Dagster for managing complex data movement and model training pipelines. Search & Discovery: Advanced ElasticSearch/OpenSearch for semantic search and large-scale indexing. AI Frameworks: Exposure to LangChain, LlamaIndex, or Hugging Face libraries ...

LLMs including prompting techniques, embeddings, and RAG architectures Experience building and deploying AI agents or autonomous systems in production Knowledge of vector databases and semantic search systems Contributions to open‐source AI/ML projects Infrastructure Guru Experience with containerization (Docker, Kubernetes) and CI/CD pipelines Experience ...

statistics, probability, optimization, experimentation, and applied machine learning. Advanced proficiency in Python and modern AI frameworks, including PyTorch, TensorFlow, Scikit‐Learn, Hugging Face, LangChain, Semantic Kernel, and related ecosystems. Experience building and scaling enterprise AI platforms, ML infrastructure, and intelligent applications serving thousands of users. Strong understanding of model … evaluation frameworks, experimentation, reliability, and operational perfection. Expertise in cloud‐native AI development using Azure AI Foundry, Azure Machine Learning, Azure OpenAI, Azure AI Search, AWS AI Services, and modern cloud architectures. Experience implementing MLOps and LLMOps practices, including CI/CD, model lifecycle management, governance, and production operations ...

enterprise data, APIs and business workflows. Retrieval-Augmented Generation (RAG) solutions and knowledge retrieval platforms. Agentic AI applications and multi-agent workflows. AI-powered search and automation platforms. Internal AI Engineering Platforms, reusable templates and developer enablement tooling. Cloud-native applications deployed on Azure and AWS. Examples … Application Monitoring and Observability. Highly Desirable Public Sector experience. Central Government programme experience. Active SC Clearance. Agentic AI and Multi-Agent Systems. SharePoint integrations. Semantic Search and Vector Search. AI Search and Knowledge Management platforms. AI Governance and Responsible AI. Domain Driven Design (DDD). CQRS. Event ...

with cloud platforms to support scalable AI deployment. Integrate GenAI features into existing products, platforms, and business systems. Build and maintain data pipelines, vector search, embeddings, and knowledge retrieval workflows. Support experimentation, model evaluation, testing, monitoring, and optimisation. Work with software engineering teams to ensure AI solutions are reliable … prompt engineering, or AI agents. Strong Python development experience. Experience working with APIs and integrating AI services into applications. Understanding of embeddings, vector databases, semantic search, and retrieval workflows. Experience with cloud platforms such as AWS, Azure, or GCP. Good understanding of software engineering best practices, testing, version ...

with cloud platforms to support scalable AI deployment. Integrate GenAI features into existing products, platforms, and business systems. Build and maintain data pipelines, vector search, embeddings, and knowledge retrieval workflows. Support experimentation, model evaluation, testing, monitoring, and optimisation. Work with software engineering teams to ensure AI solutions are reliable … prompt engineering, or AI agents. Strong Python development experience. Experience working with APIs and integrating AI services into applications. Understanding of embeddings, vector databases, semantic search, and retrieval workflows. Experience with cloud platforms such as AWS, Azure, or GCP. Good understanding of software engineering best practices, testing, version ...

ship AI-powered features for our clients' Python web applications. You'll take LLM and machine learning ideas from prototype to production, adding AI search, intelligent agents, and automation to Django and FastAPI products. You'll work closely with our senior Python engineers and with client teams. Key responsibilities … Design and build LLM-powered features such as retrieval-augmented generation (RAG), semantic search, chat assistants, and AI agents- Integrate LLM APIs (e.g. OpenAI, Anthropic, Gemini) and open-source models into Django and FastAPI services- Build and maintain data pipelines for document ingestion, chunking, embeddings, and vector search ...

operating as part of engineering teams. Generative AI & RAG: Proven experience working with LLMs and designing Retrieval-Augmented Generation (RAG) architectures, including knowledge of semantic search and vector retrieval. AICA Proficiency: Experience using AI-assisted and agentic coding tools to accelerate development, with the ability to articulate ...

organization develops foundational AI technologies that power experiences such as LeapSpace , Elsevier’s AI-powered research assistant, as well as Elsevier’s broader Search & AI Platform .The Platform Data Science organization works at the intersection of:Search and retrieval systemsGenerative AI and LLM applicationsAI evaluation and experimentationSemantic enrichment … agentic and multi-step AI workflows using frameworks such as LangGraph and related orchestration tooling.Apply state-of-the-art techniques in:NLPGenerative AIEmbeddings and semantic representationsRetrieval-augmented generation (RAG)AI reasoning and orchestrationRapidly evaluate emerging AI models, tooling, and frameworks to identify opportunities for product innovation.Translate applied AI research ...

orchestration components.Implement solutions using Azure OpenAI, OpenAI, AWS Bedrock, Vertex AI, and open-source LLMs.Develop and optimize RAG pipelines, vector databases, and semantic search capabilities.Collaborate with cloud, data, DevOps, and architecture teams to deploy production-ready solutions.Apply best practices in AI security, Responsible AI, governance, monitoring, and scalability.Mentor … Software Engineering, AI Engineering, or related fields.Strong hands-on experience with Python, Generative AI, Agentic AI, LLMs, and RAG architectures.Experience with LangChain, LangGraph, LlamaIndex, Semantic Kernel, or similar frameworks.Expertise in Azure OpenAI, OpenAI, AWS Bedrock, Vertex AI, or open-source LLM ecosystems.Knowledge of REST APIs, microservices, cloud platforms (Azure ...

applications Agentic AI, AI agents, intelligent assistants and AI-enabled automation Prompt engineering, AI evaluation and Responsible AI AI orchestration frameworks e.g. LangChain, Semantic Kernel, Model Context Protocol (MCP) Data engineering, pipelines, modelling and governance Advanced analytics, machine learning and statistical modelling Data visualisation and insight solutions Candidates should … machine learning and advanced analytics Cloud & Data Platforms – Microsoft Fabric, Databricks, Snowflake, Azure, AWS and GCP AI Technologies – GenAI frameworks, Agentic AI, vector databases, semantic search, knowledge retrieval and LLM application development Data & Visualisation – Power BI, Tableau, Looker and other business intelligence tools Consulting, Communication & Leadership: Ability ...

into resilient production applications. Retrieval-Augmented Generation (RAG): Design and tune enterprise-grade RAG pipelines, including chunking strategy, dense/hybrid retrieval, reranking, and semantic search. Data Platform Engineering: Build scalable, streaming, and batch data processing pipelines across modern cloud lakehouse architectures (Delta Lake, streaming architectures, and unified governance ...

about. With strong Python at the heart of everything you do, you'll work across a modern AI stack — LangGraph, LangChain, FastAPI, Azure, vector search and graph databases — building sophisticated, reliable systems that scale. You'll join a talented, multidisciplinary team that values engineering craft as much as innovation … grounding, context management, evaluation, reliability and performance. Experience in any of the following would be particularly valuable: complex RAG architectures (multi-stage retrieval, hybrid search, reranking, agent-driven retrieval); graph databases and GraphRAG, particularly ArangoDB; Azure AI services (Azure OpenAI, AI Search, Cosmos DB, AKS, Functions); vector/ ...

Generation (RAG) and AI applicationsAgentic AI, AI agents, intelligent assistants and AI-enabled automationPrompt engineering, AI evaluation and Responsible AIAI orchestration frameworks e.g. LangChain, Semantic Kernel, Model Context Protocol (MCP)Data engineering, pipelines, modelling and governanceAdvanced analytics, machine learning and statistical modellingData visualisation and insight solutionsCandidates should understand … Spark, machine learning and advanced analyticsCloud & Data Platforms – Microsoft Fabric, Databricks, Snowflake, Azure, AWS and GCPAI Technologies – GenAI frameworks, Agentic AI, vector databases, semantic search, knowledge retrieval and LLM application developmentData & Visualisation – Power BI, Tableau, Looker and other business intelligence toolsConsulting, Communication & Leadership:Ability to work effectively with ...

Generation (RAG) and AI applicationsAgentic AI, AI agents, intelligent assistants and AI-enabled automationPrompt engineering, AI evaluation and Responsible AIAI orchestration frameworks e.g. LangChain, Semantic Kernel, Model Context Protocol (MCP)Data engineering, pipelines, modelling and governanceAdvanced analytics, machine learning and statistical modellingData visualisation and insight solutionsCandidates should understand … Spark, machine learning and advanced analyticsCloud & Data Platforms – Microsoft Fabric, Databricks, Snowflake, Azure, AWS and GCPAI Technologies – GenAI frameworks, Agentic AI, vector databases, semantic search, knowledge retrieval and LLM application developmentData & Visualisation – Power BI, Tableau, Looker and other business intelligence toolsConsulting, Communication & Leadership:Ability to work effectively with ...

productionization of AI/ML models, including large language models and agentic systems, retrieval-augmented generation, document AI and structured extraction, embeddings and semantic search, tree-based and classical machine learning, time series forecasting, and mathematical optimization. Support agentic and LLM-based pipelines end-to-end: retrieval, extraction ...

/ML & GenAI solutions Strong Python & Java skills Experience with LangChain, LlamaIndex, CrewAI, or similar Knowledge of RAG, vector databases, and semantic search AWS cloud expertise (Bedrock, SageMaker preferred) Experience with APIs, microservices, and cloud-native architecture Nice to Have AWS certifications (ML/Architect) Experience with MLOps ...

challenges from first principles. Ability to commute to a London office once per week. Desirable Experience AI orchestration and workflow frameworks. Knowledge graph or semantic search technologies. Model fine-tuning and evaluation. MLOps, CI/CD, containerisation, and cloud-native deployment practices. Experience developing autonomous agents and reasoning ...

agent orchestration frameworks, workflow automation platforms, or tool‐augmented LLM applications Experience with context engineering techniques, including retrieval strategies, embeddings, vector databases, knowledge graphs, semantic search, memory management, and grounding approaches Experience building evaluation frameworks for AI applications, including golden datasets, automated scoring, human review workflows, red teaming ...

evaluation (eval datasets, automated and expert-in-the-loop scoring, regression testing). Experience with API integration, retrieval layers, knowledge graphs, ontologies, or semantic search. Daily use of AI coding agents (Claude Code, GitHub Copilot, Databricks AI Assistant) as the primary mode of development. Preferred Experience Prior experience ...

production, including multi-agent orchestration, tool use via MCP, agent memory and agentic RAG.Depth in NLP, such as information extraction, classification, summarisation and semantic search, or fine-tuning LLMs. Experience building shared AI platforms or reusable capabilities consumed by multiple product teams, such as internal MCP servers … agent tooling. Experience with vector search at scale, using tools such as OpenSearch, Amazon S3 Vectors or pgvector. Familiarity with AI governance and regulation, and with putting responsible AI practices into day-to-day engineering. An advanced degree in computer science, data science, linguistics or a related quantitative field ...