1 to 25 of 42 Permanent Retrieval-Augmented Generation Jobs in the Midlands

call for an AI agent that orchestrates data sources and tools; another may require a Retrieval-Augmented Generation (RAG) system over internal knowledge; another may be an AI layer embedded into an existing business process or platform. What stays consistent across … apply will be required for this role.Key responsibilities:Design and build the appropriate AI solution for each initiative, whether that is an agent, a RAG pipeline, a document processing system, or something elseIntegrate foundation models (via API) into business applications and workflowsDevelop MCP (Model Context Protocol) servers and tool integrations ...

field. Demonstrable experience of deploying modern AI/ML solutions into production, including prompt engineering, retrieval-augmented generation (RAG), model evaluation, and monitoring using metrics (e.g. precision, recall, NDCG and drift detection). Strong Python skills with a grounding in software engineering best practices ...

field. Demonstrable experience of deploying modern AI/ML solutions into production, including prompt engineering, retrieval-augmented generation (RAG), model evaluation, and monitoring using metrics (e.g. precision, recall, NDCG and drift detection). Strong Python skills with a grounding in software engineering best practices ...

principles and engineering excellence. Experience with the latest AI engineering approaches such as prompt engineering, retrieval-augmented generation (RAG), agentic AI, model evaluation, and monitoring using metrics (e.g. precision, recall, NDCG and drift detection). Strong Python skills with a grounding in software engineering ...

Azure environment.Define technical standards and best practices for developing advanced Generative AI applications, encompassing complex Retrieval-Augmented Generation (RAG) architectures, prompt and context engineering, and Large Language Model (LLM) integration strategies.Architect innovative Agentic AI systems, establishing frameworks for orchestration, memory management, and robust evaluation.Shape … within Azure environments, with a focus on technical escalation and resolution.Expertise in establishing technical standards and best practices for advanced Generative AI applications, encompassing RAG architectures, prompt engineering, and LLM integration strategies.Experience in architecting and developing Agentic AI systems, including frameworks for orchestration, memory management, and performance evaluation.Advanced knowledge ...

environment. Define technical standards and best practices for developing advanced Generative AI applications, encompassing complex Retrieval-Augmented Generation (RAG) architectures, prompt and context engineering, and Large Language Model (LLM) integration strategies. Architect innovative Agentic AI systems, establishing frameworks for orchestration, memory management, and robust … Azure environments, with a focus on technical escalation and resolution. Expertise in establishing technical standards and best practices for advanced Generative AI applications, encompassing RAG architectures, prompt engineering, and LLM integration strategies. Experience in architecting and developing Agentic AI systems, including frameworks for orchestration, memory management, and performance evaluation. Advanced ...

reinforcement learning and LLMs. Experience with the latest AI engineering approaches such as prompt engineering, retrieval-augmented generation (RAG) and agentic AI. Strong Python skills with a grounding in software engineering best practices (CI/CD, testing, code reviews etc). Expertise in data ...

reinforcement learning and LLMs. Experience with the latest AI engineering approaches such as prompt engineering, retrieval-augmented generation (RAG), and agentic AI. Strong Python skills with a grounding in software engineering best practices (CI/CD, testing, code reviews etc). Expertise in data ...

understanding of generative AI and the modern AI stack, including large language models, prompt engineering, retrieval-augmented generation (RAG), vector databases, evaluation/monitoring and MLOps principles, with the ability to shape investment cases and operating models at scale.Extensive experience providing executive oversight ...

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 ...

build analytical tools, AI-assisted insight products, and intelligent automation — including prompt engineering and retrieval-augmented generation (RAG). Validate all AI and model outputs for accuracy, bias, and reliability before they reach stakeholders.Translate reporting and analytical findings into clear, business-ready insight … data analysis, modelling, and scripting.Machine learning — knowledge in building and applying supervised and unsupervised models to real business problems.Generative AI and LLMs — prompt engineering, RAG, and applied use of LLM tooling in an analytical context.Automation tooling — experience with platforms such as Power Platform, Power Automate, or equivalent.Communication, business requirements ...

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 ...

LLMs and agentic AI. Experience with the latest AI engineering approaches such as prompt engineering, retrieval-augmented generation (RAG) and orchestration of agentic AI systems. Expertise in data engineering for AI: handling large-scale, unstructured, and multimodal data, and integrating non-traditional data sources. ...

most cutting-edge defence and aerospace projects in the UK? If the prospect of developing software for projects such as a sixth-generation stealth fighter jet or a revolutionary Laser Directed Energy product excites you, then Leonardo would like to hear from you. Leonardo is seeking to recruit … fine-tuning large language models (LLMs). Experience building scalable pipelines, including APIs, vector databases, retrieval-augmented generation (RAG), and orchestration frameworks. Experience curating datasets, managing embeddings, and ensuring security, privacy, and responsible AI practices. Experience in prompt engineering, with the ability to design ...

data pipelines and platform components Experience of coaching and mentoring data engineers to achieve successful outcomes Familiarity with AI-focused data engineering, including RAG systems, vector databases, embedding pipelines, and semantic indexing A proven record of collaboration with colleagues and stakeholders Process mapping and supporting documentation production Test plan formulation ...

integrating Generative AI/LLM functionality directly into software products Working with technologies such as OpenAI, Azure OpenAI, Claude, Gemini or local models Exploring RAG, AI agents, Semantic Kernel, MCP, vector search and intelligent workflows Engineering around the realities of production AI: accuracy, security, validation, guardrails, latency and cost Working ...

multi-cloud or hybrid environmentsExposure to Infrastructure as Code (e.g., Terraform, CloudFormation)Experience in large-scale, complex, or regulated environmentsKnowledge of vector databases and RAG architectures for building internal SRE knowledge assistants.Knowledge of Generative AI and LLM platforms (e.g., Claude, Amazon Bedrock)Person SpecificationStrong technical authority with the ability ...