1 to 25 of 68 Permanent Semantic Search Jobs in England

## Director, Search & AI EvaluationApplylocations: London: Amsterdamtime type: Full timeposted on: Posted Todayjob requisition id: R113861**D****irector,** **Search & AI evaluation****Ready to lead a data science organisation that pushes the boundaries of what intelligent systems can achieve? Do you thrive on shaping strategy, inspiring teams, and delivering … mission is to help researchers, clinicians, and life sciences professionals advance discovery and improve health outcomes through trusted content, data, and analytics. Our Search & Evaluation organization plays a critical role in shaping the future of AI-powered discovery by building intelligent retrieval, ranking, and evaluation systems that power trusted ...

multinational institutions. Задачи: Own and evolve Corestream, Kharon's risk intelligence feed for entity-level insights and topic-based risk narratives; Redesign and improve semantic search capabilities using BM25 tuning, semantic distance, RAG, and reranking; Integrate Kharon's risk analysis outputs into the search layer … graph-based insights surface meaningfully in search results and sub-network exploration; Drive the evolution of the narrative generation pipeline that transforms graph and analytical data into accurate written intelligence for customers; Partner with Product and Engineering to own the Corestream roadmap and advance it; Optimize the metrics defining ...

enabling business information to be effectively discovered, understood and consumed by AI agents. The role sits at the intersection of data science, information retrieval, semantic search and AI, with a focus on improving how enterprise knowledge is represented, retrieved and evaluated. Key Responsibilities ● Design and optimise enterprise knowledge … retrieval capabilities. ● Develop and improve taxonomy and semantic models to support enterprise search. ● Apply metadata enrichment techniques to improve knowledge discovery. ● Develop and optimise semantic retrieval and search relevance. ● Work with embedding models and other AI techniques to improve information retrieval. ● Apply query understanding and prompt engineering ...

risk intelligence feed — a dynamic platform that surfaces entity-level insights and topic-based risk narratives to customers Redesigning and improving Kharon's semantic search capabilities, opening new avenues for content discovery — drawing on modern approaches including BM25 tuning, semantic distance, RAG, and reranking Integrating Kharon … risk analysis outputs into the search layer, ensuring that graph-based insights surface meaningfully within search results and sub-network exploration Driving the evolution of Kharon's narrative generation pipeline — how we take graph and analytical data and transform it into compelling, accurate written intelligence for customers Partnering ...

Agentic AI solutions Develop AI Agents and Multi-Agent Systems Work with LangChain, LangGraph and MCP Build intelligent retrieval using Embeddings, Vector Databases and Semantic Search Apply Prompt & Context Engineering Integrate AI solutions through REST APIs and cloud platforms Test, evaluate and optimise LLM and AI Agent performance … enterprise environments Cloud experience across Azure, AWS or GCP Understanding of LLM evaluation and testing Nice to have: Enterprise Knowledge Base/AI Search experience Knowledge Graphs Semantic Search Responsible AI AI Evals Token optimisation/cost management Financial Services experience Deadline for applications is Tuesday ...

Operating Model programme for a central government department. Where the Lead Technical Architect sets direction, you turn it into working, production-grade systems - semantic search, RAG pipelines, and broader generative AI capability - integrated into complex Legacy and multi-cloud environments handling high-volume, sensitive public sector data. … direct hands-on depth across the AI engineering stack is essential: - Strong general-purpose programming (most commonly Python) applied to AI/ML systems - Semantic search, vector/embedding infrastructure, and RAG pipeline construction - LLM orchestration and agentic frameworks (eg LangChain/LlamaIndex-style tooling, multi-agent patterns ...

build systems across: Large language models and multi-model architectures Natural language processing and information extraction Agentic and automated research workflows Retrieval, embeddings and semantic search Narrative detection and clustering Multilingual text intelligence Large-scale data and model pipelines Model evaluation, observability and monitoring Production ML infrastructure … reached real users. Worked with transformer architectures or fine-tuned language models. Designed evaluation frameworks for generative AI systems. Built retrieval, RAG, embedding or semantic-search systems. Worked with large, noisy or multilingual text datasets. Built high-throughput or low-latency data pipelines. Experience operating machine learning models ...

Design, build, integrate, deploy, and operate production AI/ML and LLM-based services for legal research, analytics, and content use cases. Implement RAG, semantic search, embeddings‐based retrieval, ranking, summarisation, classification, content enrichment, and citation‐aware AI capabilities where appropriate. Design and implement agentic workflows, tool orchestration … model lifecycle management, CI/CD, testing, monitoring, release management, observability, and operational support. Practical experience with LLM‐based capabilities, including retrieval‐augmented generation, semantic search, embeddings, prompt design, evaluation, guardrails, and observability. Experience with agentic workflows, tool orchestration, and multi‐step AI processes. Strong AWS knowledge, including ...

Design, build, integrate, deploy, and operate production AI/ML and LLM-based services for legal research, analytics, and content use cases. Implement RAG, semantic search, embeddings-based retrieval, ranking, summarisation, classification, content enrichment, and citation-aware AI capabilities where appropriate. Design and implement agentic workflows, tool orchestration … model lifecycle management, CI/CD, testing, monitoring, release management, observability, and operational support. Practical experience with LLM-based capabilities, including retrieval-augmented generation, semantic search, embeddings, prompt design, evaluation, guardrails, and observability. Experience with agentic workflows, tool orchestration, and multi-step AI processes. Strong AWS knowledge, including ...

data pipelines for structured and unstructured data used in AI application workflows Support the implementation of retrieval-based and prompt-driven AI experiences, including semantic search and contextual response generation Optimize application and model integration performance for scalability, reliability, latency, and maintainability Collaborate with cross-functional teams … optimization, and scalable backend architectures Experience with unit testing, debugging, version control, and software engineering best practices Familiarity with data processing, prompt-driven workflows, semantic search, or retrieval‐based AI patterns is a strong plus Ability to work effectively in a collaborative team environment with strong problem‐solving ...

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 ...

Engineering, and will share in the ownership and delivery of cutting edge, scalable ML models. Initial workstreams will likely include: Content Recommendation and Personalisation Search Optimisation Audio Modelling You will play a crucial role in transforming our data into genuinely useful product features that empower artists and connect … Data Scientist, you will be responsible for: Developing and implementing ML-based solutions to enhance and optimise the Beatport customer experience. This may include semantic search to improve crate digging, content recommendation/personalisation engines, or leveraging our catalog for the development of best-in-class audio models ...

Intelligence Engineering and will share in the ownership and delivery of cutting‐edge, scalable ML/AI models. Initial Workstreams Recommendations and personalization Search optimization Audio modeling As a Senior ML & AI Engineer, you will be responsible for: Developing and implementing ML and AI based solutions to enhance … optimize the Beatport customer experience, including semantic search, content recommendation/personalization engines, and audio models for music professionals. Acting as a subject matter expert regarding GCP’s latest ML/AI tooling and continuously prototyping and advocating for new tooling. Owning the development cycle ...

arrangement, with compensation ranging from £90,000 to £105,000 annually. The successful candidate will own and evolve Kharon's risk intelligence platform, redesign semantic search capabilities, and partner with product and engineering teams. The role requires a strong background in data science, familiarity with semantic search ...

confidence. To learn more about Comply, visit comply.com The Role We are looking for Senior AI Data Engineers to implement and operationalize Comply’s semantic layer — turning the ontological models defined by our ontologist and architects into working knowledge graphs, vector search infrastructure, and LLM‐powered pipelines. This … hands‐on engineering role at the intersection of knowledge representation, AI infrastructure, and data platform engineering. You will own the delivery of semantic layer components, collaborate closely with application and data engineering teams, and ensure that AI‐ready data products are reliable, performant, and adopted in practice. You will ...

Multi-agent systems Tool calling Function orchestration Workflow automation Planning and reasoning patterns Human-in-the-loop design Knowledge & Retrieval RAG architectures Vector databases Semantic search Enterprise search Document ingestion Knowledge graphs (advantageous) Integration APIs Event-driven architectures Microsoft ecosystem ServiceNow CRM/ERP integration Identity management ...

equivalent orchestration framework, plus the judgement to know when to use none of them. RAG and retrieval: Chunking strategy, embeddings, hybrid and semantic search, re‐ranking, citation and provenance, natural‐language‐to‐query translation. Data platforms: NoSQL databases such as MongoDB (aggregation pipelines, Atlas Search, Atlas Vector … Search) or a strong equivalent, plus SQL. Comfortable designing schemas for agent retrieval, not just for OLTP. Data engineering: Building ingestion and enrichment pipelines over messy structured and unstructured sources, documents, PDFs, object storage. Evaluation: Building eval harnesses and eval datasets, LLM‐as‐judge with its limits understood, regression ...

frameworks such as PyTorch, TensorFlow, and Keras. Develop Retrieval-Augmented Generation (RAG) applications using LangChain, Hugging Face, and LlamaIndex. Implement and optimize vector database, semantic search, and hybrid search solutions for AI-powered applications. Perform model fine-tuning using advanced techniques such as LoRA and QLoRA … repositories Proficiency in LangChain and LlamaIndex for building AI-powered workflows Experience implementing Retrieval-Augmented Generation (RAG) frameworks Knowledge of Vector Databases and Hybrid Search techniques Strong understanding of Natural Language Processing (NLP) concepts and applications Experience with data processing, transformation, and feature engineering Expertise in developing and integrating ...

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 systems Generative AI and LLM applications AI evaluation … experimentation Semantic enrichment and knowledge systems Scalable AI platforms and intelligent workflows About the role We are looking for a Data Scientist III to help design, build, and evaluate advanced AI capabilities supporting LeapSpace and Elsevier’s Search & AI Platform initiatives. This role focuses on applied AI development ...

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 … 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 ...

libraries such as Transformers, LangChain, scikit-learn, PyTorch, or TensorFlow. Experience with OpenAI (GPT-4), Claude, Mistral, LLaMA, or similar models. Knowledge of vector search, embedding models (e.g., BERT, Sentence Transformers), and semantic search techniques. Ability to build scalable AI workflows and deploy them via APIs ...

/ELT pipelines using Python and SQL Work with BigQuery, Google Spanner, Neo4j and Vector Databases Design and implement Knowledge Graphs Develop enterprise search, indexing and retrieval capabilities Build data models, metadata and enrichment processes Implement event-driven and real-time data architectures Ensure data quality, governance, lineage … Experience with BigQuery, Spanner and/or similar cloud data platforms Hands-on Neo4j/Knowledge Graph experience Experience with Vector Databases, Search or Indexing Strong understanding of data modelling and governance Experience supporting AI, LLM or RAG use cases Nice to have: Enterprise Knowledge Management/Knowledge Base ...

Claude) into Java services via REST/SDK — for use cases such as document extraction, policy summarisation, or intelligent routing. Build or consume vector-search capabilities (e.g., OpenSearch, Pinecone) for retrieval-augmented generation (RAG) patterns. Use AI coding assistants (Claude Code, GitHub Copilot, Cursor, etc.) fluently as part … personal project or hackathon context. Familiarity with REST-based AI APIs (OpenAI, Anthropic, Google Vertex AI, or equivalent). Basic understanding of embeddings, semantic search, and token-based context management. Comfortable reading Python code or notebooks (most AI libraries are Python-first); ability to translate patterns into Java ...

distillation of LLMs (e.g., GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit-learn, XGBoost). Experience with data storage for AI, vector databases, semantic search, and knowledge graphs. Contributions to open-source AI projects or research publications. Familiarity with AI security, privacy, and compliance standards e.g. ISO42001. ...

insights for diverse engineering and business stakeholders. Knowledge of Model Context Protocol (MCP) and agent communication standards Experience with vector databases, RAG architectures, and semantic search systems Familiarity with cloud platforms (AWS, Azure, GCP) and their AI services Experience in SaaS or enterprise software companies Our Commitment ...