26 to 50 of 93 Retrieval-Augmented Generation Jobs in the UK excluding London

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 ...

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 ...

play a key role in building intelligent, scalable features, leveraging the latest in LLMs, retrieval-augmented generation (RAG), and cloud-native technologies. There are multiple roles available, ranging from mid-level to senior engineers, offering opportunities to both deepen technical expertise and take ownership … initiatives. What You’ll Be Doing Design and build AI-powered features using modern LLM frameworks Develop and optimise RAG pipelines (vector databases, embeddings, retrieval strategies) Integrate AI services into existing backend systems and APIs Work with structured and unstructured data to improve model outputs Collaborate with cross ...

play a key role in building intelligent, scalable features, leveraging the latest in LLMs, retrieval-augmented generation (RAG), and cloud-native technologies. There are multiple roles available, ranging from mid-level to senior engineers, offering opportunities to both deepen technical expertise and take ownership … initiatives. What You’ll Be Doing Design and build AI-powered features using modern LLM frameworks Develop and optimise RAG pipelines (vector databases, embeddings, retrieval strategies) Integrate AI services into existing backend systems and APIs Work with structured and unstructured data to improve model outputs Collaborate with cross ...

have 'under-the-hood' knowledge of Python, specifically for building high-throughput microservices and complex data pipelines using FastAPI, Pandas, and NumPy . The RAG & Agentic Expert: This is the 'Critical' requirement. Our client needs someone with deep hands-on experience building Retrieval-Augmented Generation (RAG) pipelines and Agentic frameworks . You should know how to use LangChain or LlamaIndex to create AI that can execute multi-step tasks. The Data Architect: Proficiency in Vector Databases is essential. You should be comfortable designing data persistence layers using PG Vector, Pinecone, Milvus, or Mongo ...

just proofs of concept. AI Engineer Responsibilities Own and evolve a production-ready AI platform Design and deliver AI-powered client solutions, including RAG pipelines, agent architectures, LLM integrations, and protocol-driven tooling Build scalable AI systems using modern software engineering best practice (clean code, testing, CI/CD) Develop … experience with Large Language Models (LLMs), including API integrations, prompt engineering, agent workflows, and Retrieval-Augmented Generation (RAG) systems Experience shipping AI-powered features into production environments Proficiency in at least two modern programming languages Familiarity with emerging AI tooling (e.g. MCP, A2A, Threads ...

challenges into scalable AI solutions Facilitate discovery sessions with legal professionals to identify high-value opportunities Evaluate and implement AI tools (e.g. LLMs, NLP, RAG pipelines) in production environments Ensure solutions meet high standards for accuracy, confidentiality, and risk Drive adoption through intuitive user experiences and clear value articulation Define …/ML-powered products Strong understanding of: Large Language Models (LLMs) NLP, classification, extraction, summarisation Retrieval-Augmented Generation (RAG) or similar architectures Experience working in regulated environments (e.g. legal, financial services, healthcare) Strong Agile delivery experience working with cross-functional teams Highly desirable Experience ...

challenges into scalable AI solutions Facilitate discovery sessions with legal professionals to identify high-value opportunities Evaluate and implement AI tools (e.g. LLMs, NLP, RAG pipelines) in production environments Ensure solutions meet high standards for accuracy, confidentiality, and risk Drive adoption through intuitive user experiences and clear value articulation Define …/ML-powered products Strong understanding of: Large Language Models (LLMs) NLP, classification, extraction, summarisation Retrieval-Augmented Generation (RAG) or similar architectures Experience working in regulated environments (e.g. legal, financial services, healthcare) Strong Agile delivery experience working with cross-functional teams Highly desirable Experience ...

services Contribute to the development and evolution of a production-grade AI platform Build and integrate AI-powered features such as LLM-driven services, RAG pipelines, and agent-based workflows into core systems Apply modern software engineering best practices (clean code, testing, CI/CD, observability) to all solutions Develop … Desirable: Experience integrating AI into production systems (e.g. chat interfaces, automation, intelligent search) Familiarity with Retrieval-Augmented Generation (RAG), vector databases, or embeddings Experience with cloud platforms (AWS preferred) Awareness of emerging AI tooling (e.g. MCP, A2A, Threads or similar) Open-source contributions ...

deploy Generative AI solutions using large language models (LLMs) Develop and optimise prompt engineering strategies for production use cases Build end-to-end pipelines (RAG, embeddings, vector search) for scalable GenAI applications Work with both structured and unstructured datasets, including text-heavy data Collaborate closely with data engineering and software ...

hands on experience in deploying and hosting Large Foundational Models. Experience with LLM architecture (e.g., Transformer, GANs, VAEs), Fine-tuning, Retrieval Augmented Generation techniques and Context embedding, Vector database technologies and Semantic Search techniques & tools. Experience in writing robust and efficient code in Python. ...

Weaviate, Chroma) Cloud (AWS/Azure/GCP) Docker, Kubernetes CI/CD, observability, testing for AI systems Experience Strong experience building LLM applications, RAG systems, or AI agents Understanding of prompt engineering, tool-calling, and orchestration frameworks Ability to work autonomously in a scale-up environment A product-focused ...