76 to 100 of 212 Retrieval-Augmented Generation Jobs in England

machine learning, data science or a related STEM field (degree or equivalent experience). Hands-on experience developing and deploying production-grade ML models, including advanced RAG (retrieval-augmented generation) systems. Deep expertise in Generative AI, LLMs, NLP, and Knowledge Graphs-with a track record of translating complex models into real-world business solutions. More ❯

machine learning, data science or a related STEM field (degree or equivalent experience). Hands-on experience developing and deploying production-grade ML models, including advanced RAG (retrieval-augmented generation) systems. Deep expertise in Generative AI, LLMs, NLP, and Knowledge Graphs-with a track record of translating complex models into real-world business solutions. More ❯

machine learning, data science or a related STEM field (degree or equivalent experience). Hands-on experience developing and deploying production-grade ML models, including advanced RAG (retrieval-augmented generation) systems. Deep expertise in Generative AI, LLMs, NLP, and Knowledge Graphs-with a track record of translating complex models into real-world business solutions. More ❯

AI specialists while maintaining clear documentation Build and deploy Agentic LLM-based solutions with LangGraph. Familiar with different multi agent system patterns Build and deploy LLM-based solutions using RAG Familiar with different types of databases: Relational, Graph etc Design and optimise APIs using Python and FastAPI to serve AI solutions. Familiar with GCP ecosystem and Cloudrun Build and optimise More ❯

Experience working in cloud environments (AWS, GCP, or Azure) Ability to work independently and communicate effectively in a remote team Bonus Points Experience with Hugging Face Transformers , LangChain , or RAG pipelines Knowledge of MLOps tools (e.g., MLflow, Weights & Biases, Docker, Kubernetes) Exposure to data engineering or DevOps practices Contributions to open-source AI projects or research publications What We Offer More ❯

and an understanding of how they facilitate agentic AI development. Strong practical knowledge of prompt engineering techniques and advanced LLM concepts, such as CoT reasoning, prompt chaining, iterative refinement, RAG techniques, MCP, multi-agent architectures, and a clear understanding of when each should be applied. Proven experience with Cloud technologies on GCP or AWS (GCP preferred) including serverless architectures and More ❯

applications using modern full-stack technologies. Key Responsibilities Develop and deploy AI-powered features into production systems. Build and optimise automation pipelines for scalable delivery. Implement RAG (Retrieval-Augmented Generation) and reasoning models into customer-facing applications. Collaborate with data scientists and engineers to translate technical concepts into real-world tools. Build full-stack … MERN Stack (MongoDB, Express.js, React.js, Node.js or similar..) Strong proficiency in Python, particularly for AI and ML applications. Hands-on experience with PyTorch, GenAI, and reasoning models. Experience implementing RAG architectures in production environments. Solid understanding of both SQL and NoSQL databases Experience building and consuming APIs; familiarity with CI/CD practices. Nice to Have Exposure to cloud platforms More ❯

secure cloud development practices and IAM role design. · Understanding of LLM fine-tuning, embeddings, vector stores (e.g., Pinecone, FAISS, OpenSearch). · Exposure to contact centre automation, conversational agents, or RAG pipelines. Please click here to find out more about our Key Information Documents. Please note that the documents provided contain generic information. If we are successful in finding you an More ❯

team building scalable GenAI solutions, architect Agentic AI components, and integrate them into enterprise systems.Skills- Strong experience in full-stack and cloud-native engineering (Azure preferred) Expertise in GenAI (RAG, prompt engineering, LLM APIs) Deep knowledge of Agentic AI systems and orchestration Proficiency in Python, SQL, NodeJS (React is a plus) Agile leadership and stakeholder collaboration skills Details- 6 month More ❯

LangChain, CrewAI, and vector databases. Core Responsibilities Build and iterate on LLM/agent-based prototypes (e.g., copilots, chatbots, A2A agents). Implement multi-step reasoning, memory modules, and RAG pipelines. Use frameworks like LangChain, LangGraph, CrewAI, and tools like Pinecone, FAISS. Optimize performance and ensure responsible AI practices. Deploy via cloud platforms (AWS Bedrock, Azure AI, Google Vertex). More ❯

and observability tools. Hands-on experience with languages and tools like Ruby, Java, React, GraphQL, AWS/GCP, and Kubernetes. Bonus: Experience with vector databases, LLM frameworks (e.g., LangChain, RAG), and open-source AI tools. Excellent communication, collaboration, and problem-solving skills. Strong organizational skills with a passion for continuous improvement. Agile Practices: Demonstrated success with agile product development, iterative More ❯

Data and AI strategy to executive stakeholders, customers, partners, and industry as needed. Technical Leadership Build & own common AI frameworks, AI platform and infrastructure components and multi agent architecture, RAG patterns, orchestration & governance patterns across the platform Create systems to leverage and experiment with best of breed AI innovation in the industry balancing performance, cost and quality tradeoffs Evolve process More ❯

deepen our understanding of AI and ML model behaviour, enabling the safe and effective exploitation of advanced AI. This includes contributing to initiatives such as LLM-as-a-judge, RAG evaluations, and agentic workflow assessment. To thrive in this role, you should possess a strong foundation in data science and a passion for responsible AI. We are looking for candidates More ❯

applications using modern full-stack technologies. Key Responsibilities Develop and deploy AI-powered features into production systems. Build and optimise automation pipelines for scalable delivery. Implement RAG (Retrieval-Augmented Generation) and reasoning models into customer-facing applications. Collaborate with data scientists and engineers to translate technical concepts into real-world tools. Build full-stack … MERN Stack (MongoDB, Express.js, React.js, Node.js or similar..) Strong proficiency in Python, particularly for AI and ML applications. Hands-on experience with PyTorch, GenAI, and reasoning models. Experience implementing RAG architectures in production environments. Solid understanding of both SQL and NoSQL databases Experience building and consuming APIs; familiarity with CI/CD practices. Nice to Have Exposure to cloud platforms More ❯

Pandas, Scikit-Learn, Langchain, Llamaindex, Azure AI Foundry, among others Practical experience and deep expertise with GenAI techniques such as Finetuning, Prompt engineering, prompt orchestration, retrieval methods (RAG and Knowledge graph techniques), Agentic Systems, etc. Knowledge of Agentic frameworks such as LangGraph, Azure AI Foundry Agents, Semantic Kernel Agents, etc. Knowledge of prompt orchestration and optimization techniques such More ❯

Architect multi-step agent workflows using: Semantic Kernel SDK (C# or Python) Azure OpenAI (GPT-4, function calling, chat completion) Planner and Kernel Memory APIs for reasoning and memory RAG pipelines grounded in enterprise data via Azure AI Search Microsoft 365 & Graph API Integration Enable agents to access and reason over content in: SharePoint, OneDrive, Teams, Outlook, and Planner Use More ❯

Architect multi-step agent workflows using: Semantic Kernel SDK (C# or Python) Azure OpenAI (GPT-4, function calling, chat completion) Planner and Kernel Memory APIs for reasoning and memory RAG pipelines grounded in enterprise data via Azure AI Search Microsoft 365 & Graph API Integration Enable agents to access and reason over content in: SharePoint, OneDrive, Teams, Outlook, and Planner Use More ❯

MVPs, then production ready services. - Develop, package and maintain shared Python libraries; ensuring robust and clean architecture, testing and CI/CD standards. - Design and deploy LLM based extraction, RAG and agentic workflows over large document collections, and drive value through integration of latest AI tech (agentic workflows etc.) - ️ Communicate clearly with technical and non technical stakeholders. Required Skills & Experience More ❯

Shop, Sports & Social Club and More Skills Required: Proficiency in Data Science techniques, including statistical models and ML algorithms. Expertise in NLP, with a keen understanding of LLM and RAG technologies. Strong development capabilities, particularly in Python. Experience with data exchange, processing, and storage frameworks (ETL, ESB, API, SQL, NoSQL, and Parquet). Comfort with Agile development methodologies. Excellent teamwork More ❯

tools for modelling. Working experience on Modern data platforms which involves Cloud & related technologies Keeps track of industry trends and incorporates new concepts Advanced understanding of AI concepts , LLMS, RAG, Agentic apps Advance Understanding in Data visualization and Data pipelines. Excellent understanding of traditional and distributed computing paradigm. Experience in designing and building scalable data pipelines. Should have excellent knowledge More ❯

tools for modelling. Working experience on Modern data platforms which involves Cloud & related technologies Keeps track of industry trends and incorporates new concepts Advanced understanding of AI concepts , LLMS, RAG, Agentic apps Advance Understanding in Data visualization and Data pipelines. Excellent understanding of traditional and distributed computing paradigm. Experience in designing and building scalable data pipelines. Should have excellent knowledge More ❯

DevOps : Solid grasp of microservices, Docker, Kubernetes, and CI/CD pipelines (GitHub Actions, Jenkins) AI/ML Foundations : Familiarity (or eagerness to learn) LLM libraries, vector stores, and RAG paradigms Sector specific knowledge: Experience with financial data systems or forecasting models Communication & Collaboration : Skilled at articulating technical concepts, driving consensus, and working cross-functionally Working at Allica Bank At More ❯

and Infrastructure as Code (IaC) engineering, gained through hands-on development experience. Key areas of proficiency include: Programming Languages: Python, Java and Go. AI/ML: prompt engineering, LLMs, RAG, Semantic Search, Vector Databases, etc Behavior-Driven Development (BDD) Testing: Cucumber, JBehave, Pytest-BDD, etc. CI/CD (Continuous Integration/Continuous Delivery) pipelines : Harness, Tekton, Jenkins, etc. Chaos Engineering More ❯

the universe! Kraken powers some of the most innovative global developments in energy. Were a technology company focused on creating a smart, sustainable energy system. From optimising renewable generation, creating a more intelligent grid and enabling utilities to provide excellent customer experiences, our operating system for energy is transforming the industry around the world in a way that … customer interactions, and real-time operational signals, powering the next generation of intelligent decision support. Stay on the forefront of emerging technologies in LLMs, RAG (Retrieval-Augmented Generation), reinforcement learning, and agentic workflows and translate them into practical, scalable products. What You'll Need Proven leadership experience in managing multi-disciplinary engineering … and platform engineering. Understanding of ML lifecycle from data ingestion and feature engineering to model training, evaluation, and deployment in production. Hands-on experience deploying LLM-based systems (e.g., RAG pipelines, tool calling, fine-tuning, RLHF) and integrating them into real-world applications. Experience developing customer-facing AI tools, voice-based interfaces, or agent augmentation systems is highly desirable. Strong More ❯

likely to extend this is paying £475 a day In order to be considered for this role you will need to have - LLM applications: LangChain, Pydantic-AI (similar frameworks), RAG systems, MCP servers Front end: some experience with Typescript, REACT Application servers, web servers, database servers, docker Some context: frontend (TS/React) and a backend (Python/FastAPI) leveraging More ❯