1 to 25 of 45 Remote/Hybrid Semantic Search Jobs in the UK

anyone else. Join us and you’ll keep millions of users listening by making great recommendations to each and every one of them. Spotify Search handles billions of queries across every market, language, and content type — music, podcasts, audiobooks, videos, playlists, and more. We’re looking for a Principal … Product Manager to lead the Search Personalization within our Conversation Product Area, setting the product strategy for how hundreds of millions of users find and discover content on Spotify. This is a leadership role at the intersection of search systems, AI, and user experience. ...

side colleagues, write code yourself and mentor other engineers. Your responsibilities will include: Designing, building and shipping production AI components such as RAG pipelines, semantic search, embedding infrastructure, orchestration logic and integration layers Leading engineering within your squad by setting coding standards, reviewing designs and pull requests … code. You'll need: Expert-level coding skills, typically in Python, applied to AI/ML systems in production Strong hands-on experience with semantic search, vector databases or embeddings, and building RAG pipelines Experience with LLM orchestration and agentic frameworks (e.g. LangChain, LlamaIndex or multi-agent patterns ...

Opus Recruitment Solutions Limited is partnering with a global organisation in London to recruit a Data Scientist for AI-powered knowledge discovery, intelligent search, and scientific insight generation. The role focuses on hands-on work with LLMs, retrieval systems, semantic search, NLP and embeddings, plus building ...

Specialist with digital agency experience and hands-on knowledge of GEO and AI search This is a great opportunity for you An opportunity has arisen for an SEO Specialist to join an established design and digital marketing agency providing website design, branding and digital marketing services to small … medium-sized businesses. As an SEO Specialist , you will manage organic search and generative AI optimisation across multiple client accounts, combining technical implementation, performance analysis and client consultation to improve search visibility and support business growth. This role offers hybrid working (four days in the office ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

machine learning models and artificial intelligence architectures across Ecofamilies. You will translate user behaviors, parenting journeys, and marketplace preferences into tailored, dynamic feed landscapes, semantic search algorithms, and predictive recommendation layers, ensuring ethical execution and deep alignment with UK privacy regulations. What You'll Do Core Responsibilities … build, and maintain production-ready machine learning engines that drive product, directory, and content personalization arrays for our UK community base. Natural Language Processing & Search: Implement large language model (LLM) fine-tuning processes and Retrieval-Augmented Generation (RAG) loops to enrich community forum semantic searches, automated content categorizations ...

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 ...

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 ...

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 ...