751 to 775 of 891 Retrieval-Augmented Generation Jobs in England

date with the rapidly evolving technology landscape.Responsibilities:Develop and integrate AI-powered applications using modern Microsoft and Azure technologies Build and improve RAG-based solutions, intelligent search and knowledge servicesExplore and develop AI agents capable of completing tasks and automating business processesCreate prototypes and proof of concepts to assess … machine learning or generative AI technologiesExperience working with Azure and cloud-based technologiesKnowledge of technologies such as Azure OpenAI, Microsoft Foundry, Semantic Kernel, RAG or vector databasesUnderstanding of AI agents, LLMs and modern AI development approachesStrong software engineering fundamentals, including source control, testing and CI/CDGenuine interest ...

Technology globally. You will build and operate the runtime, governance, and developer experience that product and service teams use, from model routing, caching, shared RAG services, MCP interfaces, Spec Kits, AI frameworks, Skills registries, to prompt management. You ensure AI agents and workloads run in a responsible (AI FinOps … teams build upon.What you do Design, build, and operate the AI platform runtime (Kubernetes-based services, gateways, and routing) including model routing, caching, shared RAG components, and prompt/skills management.Provide a plug-and-play surface for teams with AI FinOps, quotas, usage metering, and safety/compliance guardrails built ...

PrivateLink for cross-cloud data connectivity. AI Readiness : Practical experience architecting data infrastructure for AI/ML consumption - vector databases, embedding stores, and RAG pipeline integration. Soft Skills: Strong interpersonal skills; ability to translate complex data architecture into clear language for Business Analysts and non-technical stakeholders. Duties Data Modelling … warehouse scheduling strategies to reduce operational data spend. AI Readiness: Architect data stores purpose-built for LLM consumption - including vector databases, embedding pipelines, and RAG-compatible data structures that will serve as the foundation for the clients AI product layer. Data Contracts : Partner with Business Analysts to formally define ...

PrivateLink for cross-cloud data connectivity. AI Readiness : Practical experience architecting data infrastructure for AI/ML consumption - vector databases, embedding stores, and RAG pipeline integration. Soft Skills: Strong interpersonal skills; ability to translate complex data architecture into clear language for Business Analysts and non-technical stakeholders. Duties Data Modelling … warehouse scheduling strategies to reduce operational data spend. AI Readiness: Architect data stores purpose-built for LLM consumption - including vector databases, embedding pipelines, and RAG-compatible data structures that will serve as the foundation for the clients AI product layer. Data Contracts : Partner with Business Analysts to formally define ...

PrivateLink for cross-cloud data connectivity. AI Readiness : Practical experience architecting data infrastructure for AI/ML consumption - vector databases, embedding stores, and RAG pipeline integration. Soft Skills: Strong interpersonal skills; ability to translate complex data architecture into clear language for Business Analysts and non-technical stakeholders. Duties Data Modelling … warehouse scheduling strategies to reduce operational data spend. AI Readiness: Architect data stores purpose-built for LLM consumption - including vector databases, embedding pipelines, and RAG-compatible data structures that will serve as the foundation for Bestway's AI product layer. Data Contracts : Partner with Business Analysts to formally define ...

patterns, and PrivateLink for cross-cloud data connectivity.AI Readiness: Practical experience architecting data infrastructure for AI/ML consumption - vector databases, embedding stores, and RAG pipeline integration.Soft Skills: Strong interpersonal skills; ability to translate complex data architecture into clear language for Business Analysts and non-technical stakeholders. DutiesData Modelling Standard … implement warehouse scheduling strategies to reduce operational data spend.AI Readiness: Architect data stores purpose-built for LLM consumption - including vector databases, embedding pipelines, and RAG-compatible data structures that will serve as the foundation for Bestway's AI product layer.Data Contracts: Partner with Business Analysts to formally define and document ...

for. What you'll be working on as an AI Engineer - LLM & Agentic Systems: Designing and building AI agents and agentic workflows - combining LLMs, RAG, reasoning, memory and tool orchestration to solve real, complex business problems. Owning delivery end-to-end - from initial exploration through to production deployment, integration … with clear, real-world examples you can speak to confidently. Hands-on experience building AI agents including tool use, orchestration and multi-step reasoning - RAG systems that actually deliver, not just proofs of concept. Strong Python skills and solid machine learning foundations - able to write clean, production-grade code with ...

automated document classification, geospatial modeling of electric vehicle demand, demand forecasting, predictive maintenance, price optimization, and developing and maintaining production-grade agentic RAG systems that serve the entire firm.As a Data Scientist, you will research solutions and deliver high-quality outcomes with minimal guidance from Senior Data Scientists. You will … decisions.* Experience in any of the following AI/ML concepts: Cross Validation, Ensemble Methods, Over/Under Fitting, Accuracy/Precision/Recall, RAG, Agents.* Understanding of AI/ML performance measurement best practices including but not limited to how to build and apply a QA/QC framework ...

Proofs of Value (PoVs).Architect enterprise knowledge solutions using knowledge graphs, graph databases, semantic layers, ontologies, metadata management, and contextual data platforms.Design GraphRAG, Hybrid RAG, Agentic RAG, and contextual retrieval architectures to improve AI reasoning, explainability, and accuracy.Develop scalable ontologies, taxonomies, semantic models, and enterprise knowledge representations that … design, and enterprise knowledge representation.Deep understanding of graph databases, RDF, OWL, SPARQL, property graphs, linked data, and semantic interoperability.Experience designing and implementing GraphRAG, Hybrid RAG, contextual retrieval, vector search, semantic search, and enterprise knowledge retrieval architectures.Experience integrating enterprise knowledge systems with LLMs, AI agents, machine learning ...

GStreamer, NVIDIA NeMo). Connect video processing stages to computer-vision and multimodal AI models (for tasks like classification, embeddings, and structured output generation). Prototype and optimize codec-aware inference workflows to improve accuracy, latency, and cost. Requirements: Strong Python: Proven commercial experience writing clean, scalable … GStreamer. Familiarity with GPU-accelerated video/AI tech (CUDA, PyTorch, TensorRT, DeepStream). Experience with multimodal AI (VLMs, LLM APIs, Retrieval-Augmented Generation). Cloud and containerization skills (AWS, Docker). ...

practices, with the judgement to assess feasibility and shape sound solutions. Comfort with Python, modern LLM and agentic tooling, including model APIs, retrieval-augmented generation, and agents, and SQL/data is expected; hands-on engineering experience is a plus. A bachelor’s degree ...

organization.Shape our generative-AI direction. Own the strategy for applying LLMs and generative AI across the Experiences product — conversational trip planning, retrieval-augmented generation grounded in our first-party reviews and content, and automated content — with rigorous evaluation to control quality and cost, including ...

organization.Shape our generative-AI direction. Own the strategy for applying LLMs and generative AI across the Experiences product — conversational trip planning, retrieval-augmented generation grounded in our first-party reviews and content, and automated content — with rigorous evaluation to control quality and cost, including ...

organization. Shape our generative-AI direction. Own the strategy for applying LLMs and generative AI across the Experiences product — conversational trip planning, retrieval-augmented generation grounded in our first-party reviews and content, and automated content — with rigorous evaluation to control quality and cost ...

looking for an experienced Agentic AI Engineer to join a pioneering team building next-generation AI solutions. You'll work on cutting-edge projects involving LLMs, Neo4j, Knowledge Graphs, GraphRAG, and AI Agents , helping unlock greater context, reasoning, and automation from their data. Key Responsibilities: Design and develop … teams to deliver production-ready AI systems Required Skills: Strong Python development experience Hands-on experience with Neo4j and Knowledge Graphs Experience with LLMs, RAG, or GraphRAG architectures Understanding of AI agents and autonomous workflows Cloud experience (AWS, Azure, or GCP) Desirable: LangGraph, LangChain, CrewAI, AutoGen, or similar frameworks Experience ...

ending this year, with a hybrid schedule requiring three days onsite in London. You will lead the design, development and deployment of GenAI and RAG solutions, using Databricks, Azure OpenAI and modern data engineering practices for enterprise environments. You will build AI agents with LangChain/Graph and AutoGen, implement ...

Skills for the Artificial Intelligence Architect: Proven experience architecting solutions across Generative AI, Agentic AI, machine learning and automation Strong understanding of prompt engineering, RAG pipelines, model tuning, MLOps and LLMOps Experience delivering enterprise-scale RAG solutions using LLMs and vector databases Ability to design AI platforms that integrate with ...

Engineer to design, build, and deploy production-ready Generative AI systems. Role You will own the full lifecycle of GenAI applications, working with LLMs, RAG architectures, and agentic frameworks to deliver scalable, business-focused solutions. Responsibilities Build and deploy GenAI and RAG systems Develop agentic AI workflows Design prompts … clients and technical teams Requirements 1-3 years deploying LLM/GenAI systems in production Strong Python skills Experience with cloud platforms Knowledge of RAG, vector databases, and LLM frameworks Git and CI/CD experience Experience with LangChain and LangGraph AWS experience IT/Technology-related university degree (minimum ...

building and fine-tuning LLM-powered systems with a particular focus on editorial tone, style transfer, retrieval workflows, and multimodal generation (especially audio). You'll ship products from zero-to-one and see your ideas directly influence how millions of readers interact with our journalism. … tone alignment with our editorial voice Design, curate, and manage datasets used for fine-tuning, including versioning and annotation workflows Build and evaluate RAG pipelines that incorporate retrieval from structured content Prototype and test TTS (text-to-speech) pipelines for use in audio-first products (leveraging tools like ...

building and fine-tuning LLM-powered systems with a particular focus on editorial tone, style transfer, retrieval workflows, and multimodal generation (especially audio).You’ll ship products from zero-to-one and see your ideas directly influence how millions of readers interact with our journalism. … tone alignment with The Economist’s editorial voiceDesign, curate, and manage datasets used for fine-tuning, including versioning and annotation workflowsBuild and evaluate RAG pipelines that incorporate retrieval from structured contentPrototype and test TTS (text-to-speech) pipelines for use in audio-first products (leveraging tools like ElevenLabs ...

platform that underpins our clinets conversational assistant and wider AI initiatives. You will take responsibility for the runtime, infrastructure, and operational foundations underpinning our RAG pipelines, LLM orchestration, and vector search, built across Microsoft Azure and Microsoft Foundry. Where prototypes and proof-of-concepts exist, you will … Build and extend a scalable AI platform via Microsoft Foundry. Own and optimise Foundry solutions for accuracy, scale, and latency. Harden the production RAG pipeline, covering inference, retrieval, and ingestion. Architect Azure services using Terraform infrastructure-as-code. Scale vector database and embedding pipelines. Build ...

code development Leverage enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards Apply knowledge of tools within … platform-as-a-service environments, including Kubernetes and Cloud Foundry Familiarity with Generative AI tools and patterns — including prompt engineering, retrieval‐augmented generation, and vector search/embeddings — and safe, secure adoption practices Familiarity with financial services technology environments and enterprise‐grade controls ...

STEM field. Strong leader, able to communicate clearly at all levels. Track record deploying business critical ML models in production. Experience building and scaling RAG applications with semantic search and text embeddings. Cloud computing experience, ideally Azure, AWS, or GCP. Strong ML and data science fundamentals, including NLP. Ability … manage AI/RAG/GenAI risk and fine-tune models on proprietary datasets. Strong Python and SQL skills, with frameworks such as PyTorch and TensorFlow. If you're ready to shape AI strategy from the ground up and see it through to production, at real scale, this ...

senior technical authority for AI engineering strategy, architecture, and implementation across complex, multi-domain initiatives. Design, build and guide enterprise-grade AI systems including RAG platforms, agentic workflows, chat experiences, orchestration layers, evaluation pipelines, and reusable AI services. Establish scalable, secure, and compliant architecture patterns for Agentic solutions using industry … hands-on technical leadership across MCP, Python, TypeScript, React, APIs, OpenShift, Kubernetes, and CI/CD engineering practices. Build AI solutions including Agents, chatbots, RAG & Knowledge platforms, Skill based agents, workflow automation, and AI-enabled developer experiences Define engineering standards for prompt engineering, skill engineering, model evaluation, observability, guardrails ...

just getting started. Recognised by EGR as one of the most innovative startups in gaming, we\'re scaling fast and shaping the next generation of sports entertainment. We\'ve built a product that users genuinely love — with unlimited betting group chats, multi-game bet builders, and an experience … explain what broke and what you changed. Hands-on with an agent framework (LangGraph, LlamaIndex, Semantic Kernel, ADK or similar) and RAG in production: embedding models, vector stores, re-ranking, and knowing when a live query beats retrieval. Strong Python experience or similar, on a real engineering foundation: testing, version ...