1 to 25 of 254 Retrieval-Augmented Generation Jobs in England

Senior AI Engineer (Gen AI & RAG) Greater London, England, United Kingdom (On-site) Join an award-winning, internationally recognised B2B Consultancy as a Senior AI Engineer, where you will play a central role in delivering and evolving production-grade generative AI capabilities that power real-world business outcomes. This … intelligence layer of next-generation AI products. You will architect and implement Retrieval-Augmented Generation (RAG) pipelines, agentic workflows, and rigorous evaluation frameworks that ensure AI systems are accurate, reliable, and grounded in genuine business value. Reporting to the Director ...

solutions for documentation, SDKs, configurations and pipelines to enable rapid deployment of GenAI applications (including Retrieval-Augmented Generation (RAG) pipelines) and agents with planning, memory, and workflow orchestration Implement tools and frameworks for model versioning, experiment tracking, and lifecycle management Develop systems to monitor … secure mission-critical applications Previous experience deploying and managing LLM-model based applications and agents Exposure to vector stores such as Pinecone, GCP RAG engine, and AWS S3 Vector Buckets Exposure to cloud-native microservices architecture Familiarity with advanced AI/ML concepts and protocols, including Retrieval-Augmented ...

Technologies team. The candidate must possess deep expertise in Generative and Agentic AI, including LLMs, retrieval-augmented generation (RAG), machine learning, and interoperability standards such as the Model Context Protocol (MCP). The role involves shaping the Generative AI strategy, selecting suitable models … through modular, reusable skills and resources; defining standards for custom tools, connectors and skills ensuring reliable, secure, and consistent operation. Retrieval‐Augmented Generation (RAG) & Knowledge Architecture – expertise in RAG and knowledge‐grounded systems, including chunking, embeddings, vector databases (Pinecone, Weaviate, Chroma, pgvector, FAISS), hybrid ...

defined metrics. This role requires strong technical expertise across Generative and Agentic AI—including LLMs, retrieval-augmented generation (RAG), autonomous and multi-agent systems, and modern interoperability standards such as the Model Context Protocol (MCP)—coupled with excellent communication skills to engage with clients … agents load on demand. Ability to design custom tools, connectors, and skills enabling specialized, domain‐specific tasks reliably and safely. Retrieval‐Augmented Generation (RAG) & Knowledge Systems : Proven experience designing RAG and knowledge‐grounded systems, including chunking strategies, embeddings, vector databases (Pinecone, Weaviate, Chroma, pgvector ...

maintain data pipelines and curated datasets supporting AI, analytics, reporting and automation use cases. Develop AI-enabled solutions using generative AI, retrieval-augmented generation, predictive analytics, workflow automation and agentic AI. Build secure integrations between internal systems, databases, APIs, document repositories, market data providers … Power Query or similar reporting tools. Hands-on exposure to AI and machine learning technologies, which may include: LLM APIs Prompt engineering Retrieval-augmented generation Agentic workflow frameworks Python machine learning libraries Model testing and evaluation Understanding of the full solution life cycle, including ...

evolve the enterprise AI platform that powers AI products across the company. Develop scalable AI services supporting Agentic AI, Retrieval‐Augmented Generation (RAG), embeddings, vector search, model serving, and intelligent automation. Build orchestration layers that transform structured and unstructured enterprise data into reusable … Hands‐on experience building applications powered by Large Language Models (LLMs). Experience building Model Context Protocol (MCP) and Retrieval‐Augmented Generation (RAG) solutions, making use of embeddings, vector databases, and modern LLM orchestration frameworks. Experience building or contributing to agentic AI systems, intelligent ...

using Python, machine learning frameworks, and Generative AI/LLM components. Pipelines: Develop and optimize Retrieval-Augmented Generation (RAG) pipelines, vector databases, and scalable data processing streams. MLOps: Implement model deployment, monitoring, testing, and lifecycle management using standard CI/CD and containerized workflows. … role. Core Tech: Solid professional experience with Python and frameworks like PyTorch, TensorFlow, or Scikit-Learn. GenAI: Hands-on experience developing LLM-based applications, RAG pipelines, and prompt engineering. Infrastructure: Experience deploying applications in cloud environments (AWS, Azure, or GCP) using Docker and Kubernetes. Engineering: Strong software engineering fundamentals, including ...

pipelines, distributed training frameworks, inference/serving platforms, MLOps/LLMOps tooling, vector databases, and retrieval-augmented generation (RAG) and agentic architectures. Define and AI governance frameworks covering model risk management, responsible AI, data lineage, bias/fairness testing, explainability, and regulatory alignment … Kubeflow, MLflow, and at least one hyperscaler ML platform (SageMaker, Azure ML, or Vertex AI). Generative AI: LLM fine-tuning (LoRA/QLoRA), RAG architecture design, vector databases (Pinecone, Milvus, Weaviate), and agentic frameworks (LangChain, LangGraph, Semantic Kernel). Data pipelines: data lake/lakehouse architectures, ETL/ ...

Build scalable AI/ML and Agentic AI platforms, working across large language models (LLMs), retrieval-augmented generation (RAG), embeddings, and semantic search. Support AI deployment, governance, and integration patterns, and advise clients on platform capability and implementation strategy. Collaborate closely with engineering, data … DevOps, and business teams to deliver end-to-end solutions. Tech Environment Python, LLMs (prompt engineering, fine-tuning, RAG), LangChain/LangGraph, agent frameworks, vector databases, MLOps/LLMOps, AWS, Azure, GCP, Databricks, Kubernetes, and GPU infrastructure. What We're Looking For Proven experience designing enterprise AI or ML architectures ...

engineers who have built AI solutions around document intelligence, enterprise search, knowledge management, NLP or Retrieval-Augmented Generation (RAG), rather than purely research-focused AI. Key Responsibilities Design, build and deploy production-ready LLM and Machine Learning solutions using Azure Databricks, Azure Machine Learning … Foundry Good understanding of MLOps and LLMOps Excellent Python engineering skills with experience using frameworks such as LangChain, LangGraph, PyTorch and Pydantic Experience with RAG, vector search, embeddings, prompt engineering and enterprise retrieval architectures Experience building AI solutions within document-heavy environments such as legal, financial services, insurance ...

architecture strategy and roadmap. Build scalable AI/ML and Agentic AI platforms spanning LLMs, retrieval-augmented generation (RAG), embeddings and semantic search. Lead on deployment, governance and integration patterns, advise clients on platform capability and implementation strategy, and partner with engineering, data, DevOps … business teams to deliver end-to-end solutions. Tech environment Python, LLMs (prompt engineering, fine-tuning, RAG), LangChain/LangGraph, agent frameworks, vector databases, MLOps/LLMOps, AWS, Azure, GCP, Databricks, Kubernetes and GPU infrastructure. What we're looking for Proven enterprise AI or ML architecture design, strong cloud ...

Design and build AI-enabled platforms and intelligent workflows using large language models (LLMs), retrieval-augmented generation (RAG) pipelines and embedding models. Develop scalable backend services and AI integrations, working closely with engineering, architecture and business teams, and support deployment, governance and operational rollout … infrastructure exposure, plus experience in Financial Services or another regulated industry, is essential. Tech skills include Python, SQL, LLM development (prompt engineering, fine-tuning, RAG), LangChain/LangGraph, agent frameworks, vector databases, FastAPI, and AWS, Azure, GCP or Databricks. CI/CD, MLOps and LLMOps familiarity expected. Strong communication ...

Design and build AI-enabled platforms and intelligent workflows using large language models (LLMs), retrieval-augmented generation (RAG) pipelines and embedding models. Develop scalable backend services and AI integrations, working closely with engineering, architecture and business teams, and support deployment, governance and operational rollout … infrastructure exposure, plus experience in Financial Services or another regulated industry, is essential. Tech skills include Python, SQL, LLM development (prompt engineering, fine-tuning, RAG), LangChain/LangGraph, agent frameworks, vector databases, FastAPI, and AWS, Azure, GCP or Databricks. CI/CD, MLOps and LLMOps familiarity expected. Strong communication ...

Design and build AI-enabled platforms and intelligent workflows using large language models (LLMs), retrieval-augmented generation (RAG) pipelines and embedding models. Develop scalable backend services and AI integrations, working closely with engineering, architecture and business teams, and support deployment, governance and operational rollout … infrastructure exposure, plus experience in Financial Services or another regulated industry, is essential. Tech skills include Python, SQL, LLM development (prompt engineering, fine-tuning, RAG), LangChain/LangGraph, agent frameworks, vector databases, FastAPI, and AWS, Azure, GCP or Databricks. CI/CD, MLOps and LLMOps familiarity expected. Strong communication ...

Design and build AI-enabled platforms and intelligent workflows using large language models (LLMs), retrieval-augmented generation (RAG) pipelines and embedding models. Develop scalable backend services and AI integrations, working closely with engineering, architecture and business teams, and support deployment, governance and operational rollout … infrastructure exposure, plus experience in Financial Services or another regulated industry, is essential. Tech skills include Python, SQL, LLM development (prompt engineering, fine-tuning, RAG), LangChain/LangGraph, agent frameworks, vector databases, FastAPI, and AWS, Azure, GCP or Databricks. CI/CD, MLOps and LLMOps familiarity expected. Strong communication ...

bridging the gap between offshore edge developers and on-site operational technology (OT) engineers. Industrial AI & Machine Learning Integration (Good to have) Generative AI & RAG Architectures: Design and deploy Retrieval-Augmented Generation (RAG) pipelines to contextualize Large Language Models (LLMs) with proprietary OT data ...

Senior AI Engineer to design, build and scale Agentic AI, Generative AI and Retrieval-Augmented Generation (RAG) solutions that power intelligent, customer-facing products used globally. Working closely with Product, Platform and Engineering teams, you'll develop production-grade AI systems, autonomous agents … applications, helping shape the future of AI across the organisation. What You'll Do Design and deliver production-ready Agentic AI, Generative AI and RAG solutions. Build autonomous AI agents capable of reasoning, planning and executing complex workflows. Develop multi-agent orchestration, tool integration and advanced retrieval strategies. ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...