26 to 50 of 78 Semantic Search Jobs in London

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 ...

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 ...

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 ...

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 ...

Foundry, Azure OpenAI, Claude and open-source large language models, you'll design and deploy enterprise-scale AI products using agentic AI, RAG, vector search and machine learning technologies. You'll lead a blended team of AI engineers and vendor partners, driving delivery while establishing best practices … looking for: AI & GenAI Platform Leadership – Excellent experience designing and delivering AI solutions using Azure AI Foundry, Azure OpenAI, OpenAI, Azure AI Search, Claude and open-source LLMs, driving adoption across enterprise platforms. Engineering Leadership & Delivery – Deep software engineering expertise, strong Python skills, and hands-on delivery ...

interfaces, enabling secure and efficient interaction with enterprise systems, databases and external services. Develop compound AI architectures combining large language models with specialised models, semantic search, classification, re-ranking and deterministic workflows to improve accuracy, performance and cost efficiency. Build robust evaluation frameworks, automated testing, guardrails and observability ...

structured outputs and human‐in‐the‐loop patterns Practical experience designing and implementing RAG architectures Experience with document ingestion, chunking, embeddings and vector/semantic search Experience with prompt engineering and structured LLM outputs Experience with AI/LLM evaluation, testing and quality assurance Understanding of guardrails, grounding … GenAI/Agentic AI applications Azure AI & Cloud Services: Hands‐on experience building and deploying AI solutions on Azure, including Azure OpenAI, Azure AI Search, Azure AI Foundry, Cosmos DB, Redis, Azure Monitor/Application Insights, Managed Identity and Key Vault. #J-18808-Ljbffr ...

structured outputs and human-in-the-loop patterns Practical experience designing and implementing RAG architectures Experience with document ingestion, chunking, embeddings and vector/semantic search Experience with prompt engineering and structured LLM outputs Experience with AI/LLM evaluation, testing and quality assurance Understanding of guardrails, grounding … GenAI/Agentic AI applications Azure AI & Cloud Services: Hands-on experience building and deploying AI solutions on Azure, including Azure OpenAI, Azure AI Search, Azure AI Foundry, Cosmos DB, Redis, Azure Monitor/Application Insights, Managed Identity and Key Vault. If you receive suspicious outreach claiming ...

modelling and automated assessment systems. Develop and maintain data pipelines, services and infrastructure. Contribute to new AI capabilities, including Large Language Models (LLMs), embeddings, semantic search and Retrieval-Augmented Generation (RAG). Deploy, monitor and troubleshoot production data and machine learning systems. Work collaboratively with software engineers, data ...

including role definition, tool integration, planning, memory, and workflow orchestration using LangChain, CrewAI, AutoGen, ADK and LangGraph. Implement Retrieval-Augmented Generation (RAG) pipelines and semantic search using vector databases such as Pinecone and Chroma, including indexing, retrieval policies, and evaluation. Build and integrate agent tools (MCP) and APIs ...

multi-agent systems, including role definition, tool integration, planning, memory, and workflow orchestration using modern agent frameworks. Implement Retrieval-Augmented Generation (RAG) pipelines and semantic search with vector databases, including indexing, retrieval policies, and evaluation. Build and integrate agent tools and APIs to connect agents with external services ...

OpenAI APIs and/or Anthropic Claude. Practical experience with prompt engineering, system prompts, and evaluation techniques. Experience implementing RAG architectures, vector databases, and semantic search. Cloud & Platforms Strong experience with Google Cloud Platform (GCP), ideally Vertex AI. Experience deploying services using Docker/containerised workloads. Understanding of cloud ...

communication skills. AI & Modern Techniques Practical experience with large language models in applied or production settings. Experience with retrieval‐based approaches, including embeddings, semantic search, and ranking. Experience guiding or contributing to LLM‐powered systems involving multi‐step reasoning, tool use, or workflow orchestration. Strong judgment about when … MLOps practices, including experiment tracking, deployment, monitoring, and reproducibility. Experience collaborating with engineering teams on scalable system design. Familiarity with vector databases or search infrastructure such as Elasticsearch, OpenSearch, Qdrant, or similar. Nice to Have: Experience with search, information retrieval, recommendation, or knowledge systems at scale. Experience with ...

reasoning systems that enable Protégé to decompose complex patent questions, plan multi-step analyses, and compose insights from multiple data sources. Design retrieval and search architectures that deliver accurate, low-latency patent intelligence across both structured analytics and unstructured text corpora. Define patterns for AI-driven enrichment and classification … Python for AI service development; working knowledge of C# or equivalent compiled language for enterprise microservice integration. Hands-on experience with vector databases and semantic search pipeline design. Experience with Model Context Protocol (MCP). Experience with large-scale data platforms such as Databricks and search engines ...

Experience Experience with PyTorch, TensorFlow or other machine learning frameworks. Knowledge of LLM fine-tuning, model distillation and optimisation techniques. Experience with vector databases, semantic search, knowledge graphs or related technologies. Familiarity with AI governance frameworks, security standards or ISO 42001. Contributions to open-source projects, research ...

infrastructure and datacenter teams through APIs, CLI, dashboards, and Slack. Core Agent Platform — Build the platform that powers these agents, including knowledge graphs, search and retrieval, orchestration, evaluation, and the tooling that enables agents to reason, act, and continuously improve. We’re working on something that hasn’t really … production. Depth in at least one of the following: AI agent systems, orchestration, tool use, evaluation, or grounding Knowledge graphs or graph data modeling Search, retrieval, ranking, RAG, or semantic search systems Strong backend engineering experience, including API design, service boundaries, data modeling, and integrations across complex ...

Modern Techniques Hands-on experience working with large language models (LLMs) in applied or production settings Experience with retrieval-based approaches (e.g., embeddings, semantic search, ranking) Experience building or contributing to LLM-powered systems involving multi-step reasoning, tool use, or workflow orchestration (e.g., agent-based approaches … development workflows (Git, CI/CD) Ability to contribute to scalable system design and collaborate effectively with engineering teams Experience with vector databases or search infrastructure such as Elasticsearch, OpenSearch, Qdrant or similar. Collaboration & Leadership Excellent communication skills with the ability to influence both technical and non-technical stakeholders ...

evidence A useful answer starts with relevant evidence. You will help us improve how we retrieve feedback for a product question, balancing fast interactive search with deeper analysis in background workflows. This includes evaluating how we prepare and chunk data, choose embeddings, retrieve candidates and rerank results. Discovering meaningful … knowledge of Spark and cloud data environments (e.g. AWS, Databricks) Relevant technical depth: You have practical experience in one or more of information retrieval, semantic search, natural language processing, clustering or LLM-based applications, and can apply that experience to unfamiliar problems. Evaluation and experimentation skills ...

Senior Software Engineer I – Search and AI Platform About the Team Our team is dedicated to unlocking the rich knowledge embedded within Elsevier’s content through our rich data platform; this empowers researchers, clinicians, and innovators worldwide to gain new insights, make informed decisions, and accelerate progress across research … Nice to Have Experience operationalizing LLMs or building internal AI platforms. Familiarity with observability practices (metrics, logging, alerts). Exposure to knowledge graphs or semantic search systems. U.S. National Base Pay Range U.S. National Base Pay Range: $86,600 - $144,400. Geographic differentials may apply in some locations ...