14 of 14 Retrieval-Augmented Generation Jobs in Yorkshire

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 ...

infrastructure required to run AI workloads reliably, moving client architectures from static data lakes to dynamic Vector Databases and Retrieval‐Augmented Generation (RAG) architectures. Agentic Development: Build production‐grade Agentic Workflows and custom AI agents, utilising advanced toolsets to bridge the widening market implementation … maintaining data orchestration toolsets (e.g., dbt, Apache Airflow), ETL/ELT methodologies, real‐time streaming (e.g., AWS Kinesis, Apache Kafka), Vector databases, and RAG architectures. Observability & FinOps: Experience implementing modern observability tooling (OpenTelemetry) alongside automated cost‐control systems (such as Karpenter, Infracost, OpenCost, or Cloud Custodian). Domain & Sector Experience ...

shape strategy and drive delivery within a large, complex organisation Knowledge of modern AI approaches, including areas such as Retrieval Augmented Generation (RAG), prompt engineering, observability and evaluation Experience in cyber security, fraud analytics, behavioural analytics or threat detection would be particularly valuable. This ...

Lead the implementation of AI-enabled applications and platform capabilities. Establish reusable patterns, frameworks, and best practices for AI engineering. Design and oversee LLM, RAG, and Agentic AI integrations within enterprise environments. Collaborate with architects and engineering teams to ensure scalable, secure, and maintainable solutions. Define standards for AI observability … applications on Azure, AWS, or GCP. Strong knowledge of Kubernetes, Microservices, APIs, Event-Driven Architecture, and DevOps practices. Hands-on experience with LLM integration, RAG architectures, AI orchestration frameworks, and Agentic AI solutions. Experience with AI frameworks and tools such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, or equivalent. Strong ...

DevOps best practices. Collaborate with architects, product teams, and engineers to deliver high-quality solutions. Contribute to AI-enabled application development, including LLM and RAG integrations where applicable. Required Skills 10+ years of software engineering experience. Strong hands-on expertise in Java and/or Python. Cloud-native application development … APIs. Event-driven architecture (Kafka, Event Hub, RabbitMQ, etc.). CI/CD, DevOps, and Agile delivery practices. Preferred Skills Experience with LLM integration, RAG, AI orchestration, or Agentic AI frameworks. Exposure to AI platforms such as Azure OpenAI, OpenAI, LangChain, LangGraph, or Semantic Kernel. Banking or Financial Services domain ...

pipelines. The engineer must also ensure solution quality through evaluation frameworks, ground-truth validation, and defined performance metrics. Key Responsibilities Design and buildGenAI/RAG-based applicationsusing data from multiple enterprise systems. Integrate withstructured and unstructured data sources, including databases, APIs, files, and document repositories. Develop pipelines toparse and extract ...

people with: Strong TypeScript/Node.js engineering experience Experience building and deploying production systems in AWS Experience working with LLMs, agentic workflows and RAG architectures Curiosity around emerging technologies and AI-native development Strong stakeholder engagement skills The ability to move quickly in ambiguous environments This is not a role ...

Requirements gathering from business users AI & Automation Experience with Generative AI tools Prompt engineering Basic understanding of LLMs and RAG (Retrieval Augmented Generation) AI use case identification, assessment and delivering from concept through to implementation and adoption. Familiarity with Microsoft Copilot, ChatGPT Enterprise ...

Months Rolling Contract We are looking for an experienced GenAI Python Engineer with strong hands-on experience building end-to-end Generative AI and RAG applications within enterprise environments. The successful candidate will be responsible for integrating data from multiple systems, extracting and parsing information from documents and images, working … robust retrieval pipelines, and ensuring solution quality through evaluation frameworks and ground-truth validation. Key Responsibilities Design and build enterprise GenAI and RAG-based applications using data from multiple systems. Integrate structured and unstructured data sources, including databases, APIs, files and document repositories. Develop pipelines for extracting ...

Engineer | Digital Agency | £60-70,000 NLP | LLM | RAG | AI | ML My client is currently looking for an AI Engineer , to play a key role in designing, building and embedding AI-enabled capabilities across digital products and shared platforms. You will focus on the practical application of large language models … experience building solutions using LLMs, machine learning or applied AI techniques to solve practical business problems. Experience with prompt engineering, retrieval augmented generation, semantic search, AI workflow orchestration or agent-based approaches. Experience working with business or domain data to ground, evaluate and improve ...