151 to 175 of 212 Retrieval-Augmented Generation Jobs in England

like ChatGPT, Cursor, or Windsurf. You can show how prompt tweaks changed an LLM's output to match a specific product need. Nice-to-Haves LangChain, LlamaIndex, or any RAG experiment on your GitHub. Vector database dabbling (pgvector, Pinecone). A side project people outside your family have used. Our Stack React 18 Next.js Node 20 FastAPI OpenAI & Anthropic APIs More ❯

function Comfort working in a fast-moving, ambiguous environment An eye for quality and a bias toward shipping Bonus: experience in building AI-powered tools or creative applications (e.g. RAG systems, assistants, agents, etc.) This Team You'll join a cross-functional crew under AI & Services, one of Nothing's most forward-looking divisions. It's where we explore and More ❯

with the bank's cybersecurity and regulatory standards. Accountabilities & Responsibilities Architect and implement secure AI services from lab to production, ensuring scalability and compliance Develop robust APIs for LLMs, RAG pipelines, agentic workflows and document intelligence systems Embed cybersecurity and data privacy controls across all AI workflows (e.g., encryption, anonymisation, access logging) Collaborate with the CISO function on threat modeling … Built agentic workflows using modular LLM agents with memory, planning, and tool integration Implemented Model Context Protocol (MCP) to manage secure, auditable context injection across agentic systems Experience building RAG pipelines with strict data governance and contextual integrity Familiarity with EU AI Act, FCA cybersecurity principles, and oversight of critical systems Worked directly with cybersecurity and compliance teams in regulated … LLM Fine-tuning and prompt engineering for LLMs (e.g., GPT, Gemini, Claude) Secure deployment of LLMs via APIs with input/output filtering and logging Integration of LLMs into RAG pipelines, document intelligence, and agentic workflows Use of vector databases (e.g., FAISS, Pinecone, Chroma) for semantic search and retrieval Implementation of grounding, context injection, and response validation mechanisms More ❯

with the bank's cybersecurity and regulatory standards. Accountabilities & Responsibilities Architect and implement secure AI services from lab to production, ensuring scalability and compliance Develop robust APIs for LLMs, RAG pipelines, agentic workflows and document intelligence systems Embed cybersecurity and data privacy controls across all AI workflows (e.g., encryption, anonymisation, access logging) Collaborate with the CISO function on threat modeling … Built agentic workflows using modular LLM agents with memory, planning, and tool integration Implemented Model Context Protocol (MCP) to manage secure, auditable context injection across agentic systems Experience building RAG pipelines with strict data governance and contextual integrity Familiarity with EU AI Act, FCA cybersecurity principles, and oversight of critical systems Worked directly with cybersecurity and compliance teams in regulated … LLM Fine-tuning and prompt engineering for LLMs (e.g., GPT, Gemini, Claude) Secure deployment of LLMs via APIs with input/output filtering and logging Integration of LLMs into RAG pipelines, document intelligence, and agentic workflows Use of vector databases (e.g., FAISS, Pinecone, Chroma) for semantic search and retrieval Implementation of grounding, context injection, and response validation mechanisms More ❯

Be the senior voice on AI in leadership forums - shaping how data science drives strategic value. Build GenAI & ML Solutions Deliver enterprise-scale GenAI apps (e.g. virtual assistants, copilots, RAG systems). Develop robust LLMOps pipelines for scalable AI deployment and governance. Lead classical ML initiatives alongside engineering teams - from training to monitoring. Stay ahead of emerging trends in AI … Profile You'll Bring: Proven experience leading enterprise data science teams in complex or product-led environments. A track record of delivering GenAI solutions using LLMs, prompt engineering, and RAG architectures. Deep understanding of experimentation, statistical modelling, and advanced analytics. Hands-on experience with MLOps practices, cloud environments (AWS or Azure), and classical ML model deployment. Proficiency in Python, SQL … to innovate at scale - building something transformative and lasting. You'll also receive a competitive salary with bonus/pension/private healthcare. Ready to lead the next generation of data-driven innovation? Apply now to take the reins of our AI and data science strategy. Job Reference: compass/TP More ❯

data team and creating a culture that enables growth, development and psychological safety Exploring and applying Large Language Models (LLMs) and other modern AI techniques (e.g., embeddings, retrieval-augmented generation, summarisation) to enhance internal tools, automate workflows, or develop client-facing features Acquiring, cleaning, and integrating data from internal and external sources to enrich More ❯

data team and creating a culture that enables growth, development and psychological safety Exploring and applying Large Language Models (LLMs) and other modern AI techniques (e.g., embeddings, retrieval-augmented generation, summarisation) to enhance internal tools, automate workflows, or develop client-facing features Acquiring, cleaning, and integrating data from internal and external sources to enrich More ❯

data team and creating a culture that enables growth, development and psychological safety Exploring and applying Large Language Models (LLMs) and other modern AI techniques (e.g., embeddings, retrieval-augmented generation, summarisation) to enhance internal tools, automate workflows, or develop client-facing features Acquiring, cleaning, and integrating data from internal and external sources to enrich More ❯

and wants to shape how AI is used in the physical world. 🧠 You’ll need: 2+ years' engineering experience with a builder’s mindset Hands-on with LLMs — LangChain, RAG, agents, fine-tuning, etc. Infra knowledge (cloud, containers, relational/graph DBs) Bonus: startup/founder experience 🔧 You’ll be doing: Shipping to prod from Day 1 Building end-to More ❯

e.g., FastAPI, BentoML) for performance and scalability. Build and integrate intelligent automation systems, AI agents, and decision-support tools into core product workflows. Implement and optimise LLM-based systems, RAG pipelines, and AI agent architectures for complex property management workflows. Work with cross-functional teams to gather requirements, define AI use cases, and iterate quickly on prototypes. Integrate complementary AI … strategies for LLM-based systems. Hands-on experience with AI-assisted development tools such as Cursor, Claude Code, Codex, and GitHub Copilot for technical analysis, code generation and code review. Hands-on experience with AI/ML frameworks (PyTorch, TensorFlow, HuggingFace) and LLM orchestration tools … LangChain, LangGraph, or similar) Experience deploying ML models using containerised solutions (Docker, Kubernetes) and frameworks like BentoML or equivalent. Familiarity with vector databases and retrieval pipelines for RAG architectures. Knowledge of cloud platforms (AWS, GCP, Azure) and MLOps tooling (MLflow, Kubeflow, or similar). Familiarity with voice-to-text, IVR, and/or computer vision systems is a More ❯

About the Role Base Location: Reading, Havant, Perth, Glasgow Salary: £61,620 - £72,592 and a range of other benefits to support your family, finances and wellbeing. Working Pattern: Permanent Full Time Flexible working options available The role As a More ❯

About the Role Base Location: Reading, Havant, Perth, Glasgow Salary: £61,620 - £72,592 and a range of other benefits to support your family, finances and wellbeing. Working Pattern: Permanent Full Time Flexible working options available The role As a More ❯

edge product Your background looks something like: Engineering experience at tech and product-driven companies Shipping LLM-based solutions to production Experience with inference and evaluation platforms Experience with RAG, semantic search, vector stores, embedding Building features end-to-end with TypeScript, React.js, and Node.js As a person, you Are first and foremost abuilder. Are excited towork in-personfrom our More ❯

models in production to track performance, drift, and data quality, ensuring system reliability and uptime. Collaborate with Data Scientists to containerize and productionize models and algorithms, including those involving RAG and Graph RAG approaches. Manage and optimize our cloud infrastructure for ML workloads on platforms like Amazon Bedrock or similar, focusing on performance, cost-effectiveness, and scalability. Automate the provisioning More ❯

into 17 European markets over the next two years. Senior Data Scientist We are looking for an experienced and creative Senior Data Scientist to help shape the next generation of AI-driven products at Ophelos. You will lead the development of innovative, data-driven solutions that help improve the lives of people in financial difficulty at scale.The data … experimentation and A/B testing to optimise model performance and product outcomes Research, prototype, and productionise state-of-the-art LLM techniques - including fine-tuning, prompt engineering, and RAG - to build explainable, reliable AI products Collaborate closely with engineering, product, and operations teams to translate business problems into scalable data and AI solutions Work with a modern data stack …/TensorFlow, visualisation libraries) 4+ years of experience delivering machine learning products end-to-end in production Hands-on experience with LLMs - fine-tuning, prompt engineering, vector databases, or RAG pipelines Proven experience with A/B testing, experimentation design, or causal inference to guide product decisions Exposure to Databricks, MLflow, AWS, and PySpark (or similar technologies) is a plus More ❯

with product leaders, data engineers, software engineers, AI engineering specialists, and the Director of Data and AI, you will help develop cutting-edge solutions for Nourish's next-generation products. Role and Responsibilities: Support the design, development, and deployment of scalable AI-driven solutions. Collaborate closely with teams to understand product requirements and assist in translating them into … Help implement GenAI architectures using best-in-class frameworks and tools. Contribute to developing solutions leveraging Azure technologies, specifically Azure OpenAI API. Participate in the development and optimization of RAG architectures, embedding strategies, indexing, and chunking methods. Assist in managing evaluation frameworks and pipelines, including using tools such as llangfuse. Contribute to the documentation and sharing of best practices around … AI stack Good understanding and hands-on experience with GenAI tools and open-source and commercial large language models (LLMs). Familiarity with AI architectures such as Agentic AI, RAG (including GraphRAG) Experience with frameworks and tools such as LangChain, LlamaIndex, and evaluation pipelines (e.g., llangfuse). Strong collaborative and communication skills. A proactive learner with a strong desire to More ❯

and build powerful APIs that bring next-gen intelligence to our platform. Working across research, product, and engineering, you'll deliver real-world solutions using technologies like Retrieval-Augmented Generation and cloud-based AI tooling from Microsoft, AWS, and Google. You'll be part of a dynamic team pushing the boundaries of what's … the sports betting industry through AI, providing a vibrant and collaborative working environment. The opportunity to build on our established machine learning capabilities and help shape the next generation of AI-powered betting experiences. Access to cutting-edge tools , resources, and training to support your growth and development in the rapidly evolving AI landscape. The chance to see More ❯

complex customer projects involving co-training, fine-tuning, and various special projects. • Evaluate and improve the performance of our models on a variety of use cases (e.g., reasoning, code, RAG, tool use, agents) and across modalities (text, image, speech). • Develop complex use cases with our customers, providing guidance on prompting, evaluation, and fine-tuning, and ensuring the best production … data science. • You have 2+ years as a technical individual contributor (data scientist or software engineer) on AI-based products • You have experience in Fine Tuning LLMs, tackling advanced RAG or agentic use cases • You have deep understanding of concepts and algorithms underlying machine learning and LLMs • You're experienced with building and deploying LLMs or NLP applications • You have More ❯

London, the postdoc will work on shaping the future of AI-driven shopping experiences at Amazon. In particular, the project focuses on conversational search in shopping, developing next-generation conversational search systems in the shopping domain that seamlessly bridge natural shopping dialogues with precise product retrieval and recommendation. Key challenges include developing new methods for grounding … techniques in a variety of areas and innovate on behalf of shoppers, advertisers, and customers. In this role you will: Lead research projects in conversational search and retrieval-augmented systems Develop novel architectures combining dialogue management with product search Design and implement evaluation frameworks for agentic conversational shopping systems Publish research findings in top-tier conferences … Machine Learning, Information Retrieval or related field Strong publication record in NLP, Information Retrieval, or Conversational AI Experience with large language models and retrieval-augmented generation Research background in conversational systems or dialogue management Experience with modern deep learning frameworks (PyTorch, TensorFlow) Excellent written and verbal communication skills PREFERRED QUALIFICATIONS More ❯

Hiring: Senior AI Engineer (LLMs, Agents, RAG) Location: Remote (UK or Canada) Type: Full-time | Permanent Salary: Up to £130,000 or $240,000 Canadian Are you passionate about building real AI systems that get used in production? Interested in LLMs, RAG pipelines, and agentic software, with the freedom to make a real impact? My client are a well-funded … and fast iteration. No fluff, just outcomes. They're now looking for engineers who want to help shape that future. What You'll Be Working On: Building and optimising RAG pipelines including chunking, embeddings, and vector search Creating full LLM integrations using OpenAI, Hugging Face, and orchestration frameworks Developing robust AI agent systems with tool chaining and multi-step logic … scales in production environments What They're Looking For: 3+ years' experience as a full-stack engineer with strong Python skills Proven track record with LLMs, agentic systems, and RAG pipelines Experience with vector databases, embeddings, and semantic search Solid grasp of prompt engineering and LLM integration best practices Familiarity with containerisation, API design, and modern ML deployment tools Bonus More ❯

ll take the lead in shaping end-to-end Azure AI architectures, from discovery and PoCs through to delivery and optimisation. You'll work directly with enterprise clients, defining RAG systems, Agentic AI solutions, knowledge base integrations, and driving forward both client success and internal AI accelerators. Key Responsibilities: Design scalable, secure Azure AI architectures tailored to client needs. Work … closely with customer business and technical stakeholders. Define and deliver solutions involving RAG systems, Agentic AI, knowledge bases, and MCP integrations. Lead pre-sales engagements including PoCs, workshops, and stakeholder briefings. Collaborate with engineers, analysts, and scientists to guide delivery. Ensure best practice in responsible AI, data management, and security. Monitor performance and optimise ongoing AI solutions. Contribute to internal More ❯

We are looking for brilliant engineers to join our team at Magentic. We're pushing the boundaries of AI with next-generation agentic systems that can manage entire workflows. We're focusing on a three trillion dollar market of supply chains and procurement. Our mission is to make global manufacturing supply chains robust to an ever-changing world … and event buses (Kafka, Pulsar). Wrangle large, heterogeneous data sets -model, transform, and index multi-modal, multi-terabyte enterprise datasets for advanced workloads Develop enterprise-level next generation AI systems with the support of Magentic's AI specialists Ship complete customer features -from architecture and code to CI/CD, infra-as-code (Terraform), rollout, and user … contract. Thrive in an early-stage, high-ownership environment-prototype today, deploy tomorrow, iterate next week. Bonus Points Experience deploying or consuming LLM-powered services (OpenAI, open-source models, RAG, vector stores) can be a bonus. However, we consider many great candidates without previous AI experience. Familiarity with supply-chain, procurement, or manufacturing domains. Benefits Compensation and Benefits At Magentic More ❯

not, and what to test next. Translate raw data into board-level insights - clear, concise, commercially relevant, and rooted in rigorous analysis. Leverage AI/ML tools (e.g. LLMs, RAG, clustering, regression) to build smarter internal tools and identify untapped growth levers. Design, implement, and interpret A/B tests and experiments that directly inform roadmap prioritisation and go-to … automation. Comfortable using BI tools such as Tableau, PowerBI, Looker, or Lightdash, and running analysis in statistical packages. Familiarity with LLMs and AI-powered tools to support insight generation, automation, and experimentation. Proven track record of influencing senior stakeholders with data storytelling and commercial acumen Deep understanding of marketplace KPIs: CAC, LTV, take-rate, sale rate, funnel conversion More ❯

viewing in real time using image embeddings, similarity search (e.g. CLIP, vector search), and traditional CV approaches (e.g. YOLO, MobileNet). Design and implement pipelines that support retrieval-augmented generation, internal AI tools, and scalable content delivery. Experience with vector databases, agent frameworks, or data workflows is highly relevant. Own model deployment pipelines, including More ❯

support secure, air-gapped AI deployments, with a focus on NLP-based tooling * Develop pipelines for transcription ingestion and real-time analytical insight generation * Support graph and RAG-based inference layers using data from structured and unstructured sources * Build and expose APIs for frontend consumption, enabling natural-language querying, dynamic visualisation, and reporting * Ensure system portability via containerisation More ❯