1 to 25 of 33 Permanent Retrieval-Augmented Generation Jobs in the North of England

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 ...

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 ...

architecture and hands-on delivery across Generative AI, Large Language Models (LLMs), Agentic AI and Retrieval-Augmented Generation (RAG). Key Responsibilities: Lead AI strategy and executive advisory engagements Architect enterprise-grade Generative AI and Agentic AI solutions Design multi-agent systems, orchestration frameworks … RAG pipelines Own end-to-end solution architecture from discovery to implementation Shape AI-led deal pursuits, presales and business cases Define AI operating models, governance and responsible AI frameworks Integrate AI solutions with ERP, CRM and enterprise platforms Required Experience: Strong consulting background in AI/digital transformation Deep ...

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. ...

Technical Skills Genuine interest in AI/LLM technologies with some hands-on experience integrating APIs (e.g., OpenAI, Azure OpenAI) into applications Familiarity with RAG patterns, vector search, and document processing pipelines for AI-enhanced applications Experience with data engineering tooling such as Microsoft Fabric for building analytics and reporting ...

choices directly shape what we ship to clients.Day to day, you'll work across the full delivery lifecycle: designing multi-agent orchestration pipelines and RAG patterns on the backend, building performant React frontends that surface AI-driven insights, and collaborating with customers, product owners, and infrastructure specialists to integrate generative … with generative AI components, ensuring high availability and resilience across Azure-hosted environments Architect complex backend pipelines in Python, incorporating multi-agent systems, advanced RAG patterns, and prompt engineering strategies within Azure’s enterprise AI ecosystem (Azure OpenAI, Azure AI Search, Document Intelligence) Establish and enforce best practices ...

services, model integration, model monitoring, evaluations, and deployments Integrate and optimize LLMs for specific use cases in business planning, including prompt engineering and RAG implementation Build conversational interfaces and agentic workflows that make complex planning tasks accessible through natural language Implement evaluation frameworks to measure and improve GenAI feature quality … front-end development with React, TypeScript, and modern web technologies Understanding of RESTful API design, microservices architecture, and cloud infrastructure Experience with prompt engineering, RAG systems Strong foundation in ML fundamentals, including NLP, time-series analysis, or recommender systems Familiarity with containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines ...

Generative AI solutions across data, application and cloud infrastructure layers. This is a senior-level architecture role focused on delivering production-ready LLM, RAG and agentic AI platforms within complex enterprise environments. Key Responsibilities Define and own AI reference architecture across data ingestion, model orchestration, inference services and application integration … Architect and deploy LLM solutions (GPT, BERT, Transformers) including RAG pipelines and vector databases Lead LLMOps/MLOps strategy including model lifecycle, CI/CD for ML, model registry and monitoring Design scalable cloud-native AI solutions in Azure, AWS or GCP Ensure governance, Responsible AI, security, compliance ...

Science, or a related field Interest in AI, machine learning, and modern software development practices Curiosity and eagerness to learn about RAG (Retrieval-Augmented Generation) and other AI applications Strong analytical skills and ability to work collaboratively in a team environment Proactive mindset, ready ...

break into AI properly, not just talk about it? I'm working with a team building real world AI applications using LLMs and RAG and they're open to junior to mid-level developers who've already started exploring this space (even through personal projects) click apply for full ...

Looking For Essential: Strong background in software engineering, architecture or technical delivery Clear pivot into AI, with hands-on understanding of LLMs, agent architectures, RAG and modern AI ecosystems Experience leading client AI or digital transformation engagements Consultancy or professional services background Ability to translate AI capability into commercial value ...

developing technical talent. Advise on internal AI platform strategy and client positioning. Apply modern AI/ML approaches, including LLMs, agent architectures, and RAG techniques, in delivery and practice strategy. Collaborate with stakeholders to translate technical capabilities into practical business impact. What We're Looking For: Essential: Strong background ...

Forward Deployed Engineer Generative AI/LLM Deployment GenAI | LangChain | RAG | AWS | Kubernetes | Customer-Facing We are hiring a Forward Deployed Engineer to lead the deployment of production-grade Generative AI and LLM solutions into enterprise environments. This is a hands-on, customer-facing role focused on building and deploying … RAG pipelines, multi-agent systems and fine-tuned LLM applications using tools such as LangChain and LangGraph. You will embed with client teams, own technical delivery from prototype to production, and demonstrate measurable KPI impact. Key Responsibilities Design and deploy GenAI/LLM applications (RAG, agentic AI, fine-tuning) Lead ...