1 to 25 of 209 Retrieval-Augmented Generation Jobs in England

focused on building the intelligence layer of advanced AI products. You will design and implement Retrieval-Augmented Generation (RAG) pipelines, agentic workflows, and robust evaluation frameworks that ensure AI systems are accurate and aligned with real-world business needs. Reporting to the Director … with AI Platform Engineering to ensure solutions are scalable, maintainable, and production ready. Design, build, and train production-grade generative AI systems. Implement robust RAG pipelines that effectively leverage proprietary and domain-specific content. Develop agentic and workflow-based AI patterns with a focus on determinism, quality, and maintainability. Apply ...

Experience with: AWS: boto3, Bedrock, SageMaker, Lambda, S3, EC2 Azure: Azure OpenAI Service, Cosmos DB Retrieval-Augmented Generation (RAG), Graph RAG Embedding models and LLM training fundamentals Damia Group Limited acts as an employment agency for permanent recruitment and employment business for the supply ...

engineers who want to remain hands-on while operating at architectural and strategic depth. You will act as the technical authority behind next-generation Generative AI and Agentic systems , working across multiple client programmes rather than being tied to a single product or domain. You will have … Claude, Gemini , as well as self-hosted and fine-tuned models where required. Define patterns for agent orchestration, memory, evaluation, and observability. Pioneer RAG & Multi-Modal Solutions Implement advanced Retrieval-Augmented Generation (RAG) pipelines using vector databases such as Pinecone and Weaviate . Build ...

Model Context Protocol (MCP). Knowledge of statistics, probability, and model evaluation techniques. experience with Retrieval-Augmented Generation (RAG) and vector databases like Pinecone or Weaviate. Proven track record of designing, developing, deploying, and managing AI/GenAI solutions in production. Additional Skills & Qualifications ...

MLOps tools such as SageMaker, Snowflake, and CI/CD pipelines. •Integrate vector databases and Retrieval-Augmented Generation (RAG) pipelines. •Ensure high-quality delivery and adherence to best practices in AI/ML engineering. Required •Minimum 7 years of experience in Information Technology. •Minimum … LLMs, and agentic frameworks. Preferred •Experience with API Gateway development and deployment on Azure/GCP. •Hands-on experience with vector databases and RAG pipelines. •Familiarity with CI/CD, DevOps, and automation tools in AI/ML contexts. •Strong problem-solving and stakeholder management skills. •Domain expertise in Banking ...

Solutions Develop and enhance GenAI applications using frameworks such as LangChain, LangGraph, or LlamaIndex , integrating with LLMs including GPT, Claude, and Gemini . Implement RAG Pipelines Support the design and delivery of Retrieval-Augmented Generation (RAG) solutions using vector databases such as Pinecone ...

difference in people’s lives. Your Mission We’re seeking a highly experienced, technically exceptional Lead AI Engineer to architect and deliver next-generation Generative AI and Agentic systems. You will drive end-to-end innovation, from model selection and orchestration design to scalable backend implementation, all while … Claude, Gemini as well as self-hosted LLMs along with MCP integrations Implement Retrieval-Augmented Generation (RAG) techniques leveraging vector databases (Pinecone/ChromaDB/Weaviate/pgvector/etc), search engines such as ElasticSearch/Solr using both TF/IDF BM25 based ...

looking for a highly skilled AI Architect to lead the design and implementation of AI/GenAI solutions, including LLM-based systems, RAG pipelines, vector search, and cloud-based AI deployments. Key Responsibilities: - Architect LLM-driven applications, GenAI platforms, and AI microservices. - Build and optimize Retrieval-Augmented Generation (RAG) architectures. - Integrate AI models using OpenAI GPT APIs, Hugging Face, and custom model endpoints. - Lead vector database adoption (MongoDB Atlas Vector Search, FAISS, Pinecone). - Design end-to-end AI pipelines using LangChain or LlamaIndex. - Ensure AI solutions meet enterprise-grade security, compliance ...

experience with Large Language Models (LLMs), evaluation techniques, and frameworks such as LangChain. Proficiency with Retrieval-Augmented Generation (RAG) techniques, including embeddings, vector search, and vector databases. Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field. Strong ...

solutions that sit at the heart of real business workflows. This is a hands-on contract role suited to someone who has delivered RAG pipelines, multi-agent systems and NLP-driven applications in live environments, ideally within complex or regulated domains. Location: London (2 days onsite) Contract: 6 Months (OIR35 … Python experience for ML and AI development Hands-on experience with LLMs & Generative AI , including: Retrieval-Augmented Generation (RAG) Prompt & context engineering Agentic/multi-agent architectures Experience with frameworks such as LangChain, LangGraph, LangSmith or similar Solid NLP background (tokenisation, embeddings, transformers, BERT ...

services, APIs and integrations with existing platforms Implement data pipelines, embeddings, vector search and retrieval-augmented generation (RAG) patterns Work closely with engineering, product and business stakeholders to shape AI use cases Ensure solutions meet security, governance and performance requirements Contribute to best practice … with vector databases, embeddings and modern AI architectures Comfortable working in agile, fast-moving delivery environments Nice to Have Experience with GenAI use cases (RAG, agents, copilots, document automation) Exposure to MLOps , CI/CD and monitoring of AI systems Experience in regulated or enterprise environments Consulting or client-facing ...

LlamaIndex for building LLM-powered applications Vector Databases: FAISS, Weaviate, Pinecone, Qdrant (Nice-to-Have) Retrieval-Augmented Generation (RAG): Experience building hybrid systems combining LLMs with enterprise data MLOps & Infrastructure MLOps: Model versioning, monitoring, logging Bias Detection & Mitigation Content Filtering & Moderation Explainability & Transparency ...

accelerate software development (using Github Copilot Coding Agents or similar) Desirable: Experience with prompt engineering, Retrieval-Augmented Generation (RAG), and graph databases Familiarity with multi-agent LLM systems and agentic platforms (e.g., AutoGen, CrewAI), and experience deploying LLM-based applications Experience with tools such ...

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 ...

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 ...