51 to 75 of 117 Semantic Search Jobs in England

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 ...

OpenAI or similar platforms, with exposure to techniques and frameworks such as retrieval‐augmented generation (RAG), Model Context Protocol (MCP) vector databases and embeddings, semantic search and agentic workflows. Full‐stack engineering, building web applications, dashboards, APIs and internal tools using established languages (JavaScript/Java/Python … productivity and business outcomes. Developing solutions on Microsoft Azure including App Services, Functions, Storage and managed database services, Azure AI Foundry and Azure AI Search Building and operating production services with appropriate monitoring, alerting, logging and observability tooling (e.g. New Relic), using platforms such as. Event‐driven architectures, messaging ...

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 ...

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 ...

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 … service development; working knowledge of C# or equivalent compiled language for enterprise microservice integration. Essential - Hands‐on experience with vector databases and semantic search pipeline design. Essential - Experience with Model Context Protocol (MCP). Essential - 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 ...

already be an AI specialist. Evidence of active development in this direction might include: Any applications of large language models (LLMs) including RAG or semantic search, agentic systems and structure tool use, and integrations into wider systems. Any experience of using model evaluation and monitoring for non-deterministic ...

scale GCP data platforms using BigQuery, Spanner Graph DB, Dataplex, Dataflow, Pub/Sub, BigLake and Cloud Storage. Develop architectures supporting GenAI, RAG, vector search, embeddings and Agentic AI platforms using Vertex AI and related technologies Define data models and integration patterns for Domain and Cross-Domain Digital Twins. … cloud-native data platforms. Google Cloud Expertise including: BigQuery, Dataflow, Pub/Sub Dataplex, BigLake, Cloud Storage, Vertex AI, Cloud Composer, Spanner Knowledge Graph & Semantic Modelling Data Engineering: ETL/ELT pipelines, Real-time streaming architectures, Event-driven platforms, Data observability and governance AI & Data Intelligence: RAG architectures, Vector ...

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 ...

Synapse Analytics, DevOps. Strong LLM, embeddings, RAG, prompt engineering knowledge. Expertise in secure API/event‐driven design, microservices, DDD. Experience with vector databases, semantic search, Azure ML pipelines, Power Platform, multi‐agent orchestration. Contract Details Duration: 6 months Working Model: London, Tunbridge Wells, Ipswich, Bolton (Hybrid ...

Experience We are particularly interested in candidates with experience or strong knowledge of: Multi-agent or agent-based solution architectures Retrieval-augmented generation and semantic search Data correlation and entity-relationship mapping Network telemetry and real-time operational data Vector or graph databases Context management and token optimisation ...

Python and SQL Nice to have Experience within gaming, fintech, financial services or other regulated environments AWS experience Exposure to vector databases, embeddings and semantic search Experience with GitHub Copilot, Cursor or similar AI coding assistants Experience with low code automation tools such as n8n Experience delivering training ...

Model Context Protocol) or similar frameworks. Experience with LLM providers (OpenAI, Anthropic) for inference and evaluation. Working knowledge of vector databases, embedding models, and semantic search. Experience with coding assistants (GitHub Copilot, Cursor) in a developer productivity context. Multi‐site or international delivery experience. What We’re Looking ...

backend services. Key responsibilities will include: Building production applications and services using Python Developing solutions using LLMs and Generative AI Creating RAG and semantic retrieval solutions over structured and unstructured information Developing intelligent workflows using AI agents and orchestration frameworks Building scalable backend APIs using FastAPI Implementing document processing … embedding and vector-search capabilities Connecting AI services with existing applications, databases and data sources Creating methods for testing and measuring the quality of AI-generated outputs Containerising and deploying applications into modern cloud environments Writing clean, maintainable and well-tested software Working closely with product, engineering and business ...