11 of 11 Permanent Semantic Search Jobs in the City of London

applicant will be penalized as a result of such a request. Job Summary We are looking for a Product Engineer to join our AI Search and Retrieval Context Engineering as we build the next generation of legal AI experiences for law firms. This is not a traditional front … expectation is broader than UI delivery. You will work close to lawyers, knowledge teams, innovation teams and technical stakeholders to understand how AI search, retrieval and agents should fit into real legal workflows. We are not building another disconnected AI chatbot. We are building trusted AI experiences that ...

credentials and adjacent platform experience will be considered. The Work Architecture & Design Design and document multi-agent architecture blueprints, agentic orchestration patterns, knowledge and semantic layer blueprints, AI governance blueprints, and AgentOps/LLMOps architecture Design the knowledge architecture underpinning agent intelligence: embeddings pipelines, vector retrieval, and semantic search — structured around commerce-specific intent, product relationships, and category hierarchies Architect the integration layer connecting agentic systems to enterprise commerce stacks (OMS, PIM, DAM, CRM, payments, pricing, fulfilment) via API gateways, event buses, and emerging agentic protocols (MCP, A2A, ACP, UCP) Lead build vs. buy decisions across ...

agent patterns, LLM-powered applications, retrieval-augmented generation (RAG) systems, and orchestration pipelines applied to commerce use cases such as personalisation, conversational commerce, intelligent search, and merchandising optimisation Architecting the integration between agentic AI systems and enterprise platforms — including OMS, PIM, CRM, payments, and fulfilment — via APIs, event-driven … reasoned technical point of view grounded in delivery experience Designing the knowledge and data architecture that underpins agent intelligence — including embedding pipelines, vector retrieval, semantic search, and structured product data — ensuring agent outputs are accurate, grounded, and commercially reliable Embedding AI governance, observability, and responsible AI principles into ...

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 Senior Data Scientist I to help design, build, and evaluate advanced AI capabilities powering LeapSpace. This role will focus heavily on applied AI research and development, including prototyping intelligent ...

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 ...

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 ...

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 ...

role blends hands-on work with strategic oversight, guiding architecture, reviews, and mentoring while ensuring production-ready, scalable systems for RAG, retrieval, and semantic search. #J-18808-Ljbffr ...

development (Claude, Cursor etc) Worked on a broker platform or similar industry experience a huge plus Nice to haves: Spring Boot Elasticsearch Vector embeddings Semantic search Job Offer Performance based discretionary cash bonus scheme Discretionary stock grant Group Life Assurance cover Group Income Protection Occupation pension scheme based ...