51 to 75 of 172 Retrieval-Augmented Generation Jobs in the UK

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 ...

optimise Python-based AI components, including prompt orchestration, output validation, and evaluation tooling. Work with Generative and Agentic AI patterns, including LLM integration, RAG architectures, prompt-driven workflows, and AI service orchestration. Integrate AI capabilities with enterprise systems, observability tooling, and security frameworks. Design and maintain CI/CD pipelines … within cloud-native engineering environments. Support benchmarking, evaluation, experimentation, and cost optimisation for LLM workloads. Contribute to scalable RAG implementations and enterprise data access patterns. Help define reusable APIs, engineering standards, and platform documentation. Troubleshoot and optimise distributed systems and cloud-based services. Collaborate across engineering, platform, architecture, and product ...

emerging AI/ML capabilities within modern data platforms. Exposure to vector databases, embeddings, retrieval-augmented generation (RAG), and AI-ready data architectures. Retail, wholesale, supply chain, or large-scale transactional data experience. ...

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 ...

hands-on with Python and Azure , possessing a proven track record of designing and deploying Retrieval-Augmented Generation (RAG) patterns. Key Responsibilities Architect & Govern: Design and implement robust on-premises, cloud, and hybrid AI solutions, ensuring strict technical governance and best practices across … client relationships to secure consensus on complex technical architectures. Essential Skills Clearance: Active UK Government Security Check (SC). GenAI: Expertise in LLMs and RAG architectures. Python: Strong proficiency alongside PySpark for data engineering. Azure: Deep familiarity with native tools (AI Search, Document Intelligence). MLOps: Understanding of LLMOps frameworks ...

hands-on with Python and Azure , possessing a proven track record of designing and deploying Retrieval-Augmented Generation (RAG) patterns. Key Responsibilities Architect & Govern: Design and implement robust on-premises, cloud, and hybrid AI solutions, ensuring strict technical governance and best practices across … client relationships to secure consensus on complex technical architectures. Essential Skills Clearance: Active UK Government Security Check (SC). GenAI: Expertise in LLMs and RAG architectures. Python: Strong proficiency alongside PySpark for data engineering. Azure: Deep familiarity with native tools (AI Search, Document Intelligence). MLOps: Understanding of LLMOps frameworks ...

intersection of DevOps, cloud infrastructure and applied AI owning the full lifecycle of Retrieval-Augmented Generation (RAG) and LLM-powered systems across modern cloud architecture. This role is about engineering, not research. You will architect and run the infrastructure that enables AI to perform … hardened, production-ready services while shaping a reusable AI platform capable of supporting multiple products. What You'll Be Doing Design and optimise scalable RAG pipelines and vector search systems Orchestrate multi-model AI services with a focus on latency, resilience and performance Productionise GenAI workflows and ensure they operate ...

optimise Python-based AI components, including prompt orchestration, output validation, and evaluation tooling. Work with Generative and Agentic AI patterns, including LLM integration, RAG architectures, prompt-driven workflows, and AI service orchestration. Integrate AI capabilities with enterprise systems, observability tooling, and security frameworks. Design and maintain CI/CD pipelines … within cloud-native engineering environments. Support benchmarking, evaluation, experimentation, and cost optimisation for LLM workloads. Contribute to scalable RAG implementations and enterprise data access patterns. Help define reusable APIs, engineering standards, and platform documentation. Troubleshoot and optimise distributed systems and cloud-based services. Collaborate across engineering, platform, architecture, and product ...

Responsibilities Design & Deliver: Architect and implement cloud and AI-ready solutions on Azure, building repeatable patterns and integration services that support next-generation workloads. Secure AI Architecture: Develop and scale secure infrastructure architectures that allow applications to safely access both cloud-based backend services and on-premises data … Beyond the Basics): Real-world enterprise experience designing and deploying solutions centred around agentic workflows, Retrieval-Augmented Generation (RAG), and vectorisation patterns, rather than purely self-taught or personal interest projects. Software Engineering & Tech Stack: High proficiency in Python and PowerShell for building automation ...

unlock value Building solutions using Large Language Models (LLMs), Agentic/autonomous AI workflows, Retrieval-Augmented Generation (RAG), Modern AI tooling (e.g. LangChain, vector databases, APIs) Rapidly prototyping and iterating on solutions within fast-paced timelines Translating complex technical concepts into practical, commercial outcomes … Looking For Strong software engineering fundamentals (Python, APIs, version control, testing) Experience working with modern AI technologies, such as LLMs/Generative AI, RAG pipelines, Agent-based architectures Ability to work in fast-paced, ambiguous environments and deliver tangible outcomes quickly Strong communication skills and comfort working in client-facing ...

trends, governance considerations, and market developments Technical Focus Areas Agentic AI and enterprise LLM applications Retrieval-Augmented Generation (RAG) architectures AI agents and workflow orchestration AI solution architecture and systems integration Azure OpenAI, AWS Bedrock, GCP Vertex AI, or similar platforms Enterprise AI governance ...

advanced frameworks (PyTorch, TensorFlow, Hugging Face, LangChain) Oversee integration of LLMs (OpenAI, Anthropic, Gemini, Mistral) via enterprise platforms (Azure OpenAI, Vertex AI) Implement RAG architectures and manage vector databases (Pinecone, Weaviate, FAISS, Milvus) Data & Engineering Excellence Lead the evolution of modern data platforms (Databricks, Snowflake, Synapse, BigQuery) Ensure robust data ...

Agentic System Design: Build intelligent AI agents capable of autonomous task execution using LLMs and advanced reasoning frameworks. LLM Specialization: Implement RAG (Retrieval-Augmented Generation) pipelines and fine-tune models using PEFT/LoRA to create domain-specific insurance experts. Production Engineering: Design ...

experience using frameworks such as PyTorch, TensorFlow, LangChain, or similar. * Experience working with LLMs, prompt engineering, vector databases, and retrieval augmented generation (RAG) architectures. * Strong knowledge of cloud platforms including AWS or Azure. * Experience with data engineering and orchestration tools such as Databricks, Airflow ...

experience with cloud platforms (AWS, Azure, GCP) Experience with ML frameworks (PyTorch, TensorFlow, scikit-learn) and Gen AI/Agentic frameworks (LangGraph, LangChain, VectorDBs, RAG) Understanding of responsible AI practices, including guardrails, hallucination mitigation, and output quality management for AI systems Experience designing and scaling low-latency, customer-facing ...

experience with cloud platforms (AWS, Azure, GCP)Experience with ML frameworks (PyTorch, TensorFlow, scikit-learn) and Gen AI/Agentic frameworks (LangGraph, LangChain, VectorDBs, RAG)Understanding of responsible AI practices, including guardrails, hallucination mitigation, and output quality management for AI systems Experience designing and scaling low-latency, customer-facing ...

tooling) AWS/Azure cloud platforms (including AI services such as Bedrock) Data engineering tools (e.g. AWS Glue or equivalent) NLP, embeddings, vector databases, RAG architectures MLOps tooling for model deployment and monitoring Experience with distributed data systems and APIs Required Skills & Experience Several years' experience in software engineering, data ...

implementing end-to-end AI/ML solutions from development through to deployment and monitoring * Building scalable Generative AI and Agentic AI solutions, including RAG pipelines and orchestration frameworks * Implementing MLOps frameworks, governance and best practices * Translating business requirements into cloud-native AI and data architectures across Azure ...

journey The key things they are looking for are: Strong Python skills A good general understanding of LLMs Some exposure to GenAI, chatbots, NLP, RAG, prompt engineering or similar An interest in AI guardrails and responsible use of AI Good software engineering principles Awareness of security in modern development ...

Youll work at the intersection of: AI strategy Solution architecture Hands-on delivery What youll be doing: Architecture & Delivery Design and scale Generative AI, RAG, and Agentic systems in production Build enterprise AI platforms across AWS/Azure/GCP Select LLMs, vector databases, and orchestration frameworks Ensure solutions … youll need (must-have): Experience as an AI Architect/ML Architect/GenAI Architect Strong expertise in: Generative AI & LLMs, RAG (Retrieval-Augmented Generation), Agentic AI frameworks Hands-on with; LLMs (OpenAI, Llama, Claude, Mistral), Vector DBs (Pinecone, Weaviate, FAISS), Frameworks (LangChain, LlamaIndex ...