1 to 25 of 27 Retrieval-Augmented Generation Jobs in the Midlands

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),modelevaluation,andmonitoringusing metrics (e.g. precision, recall, NDCG and drift detection). Strong Python skills with a grounding in software engineering best practices(CI/ ...

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 ...

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 ...

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 ...

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 ...

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 ...

Azure, including SaaS and PaaS offerings. Hands-on experience building and deploying with modern AI, including large language models and agentic AI (prompt engineering, RAG, tool and function calling, orchestration), on the Microsoft AI stack such as Azure OpenAI, Azure AI Foundry and Azure AI services. Proven experience of technical ...

agentic AI systems for enterprise clients, in partnership with leading frontier-model providers. We already support clients from different verticals to have agentic and RAG systems live in production across healthcare, financial services, retail and global logistics. As a Forward Deployed AI Engineer you work embedded in the client … least one of: Anthropic Agent SDK, LangGraph, LlamaIndex, or an equivalent orchestration framework, plus the judgement to know when to use none of them. RAG and retrieval: Chunking strategy, embeddings, hybrid and semantic search, re‐ranking, citation and provenance, natural‐language‐to‐query translation. Data platforms: NoSQL databases ...

improve Design and ship multi-agent systems and production-grade automations that measurably improve how internal teams and customer-facing staff work Build RAG pipelines for internal knowledge retrieval that ground agents in real organisational context Keep the AI tooling stack healthy as usage scales across 2,500+ … development Hands-on experience building multi-agent systems using agentic frameworks — LangChain, LangGraph, MCP, Google ADK or similar Experience with RAG pipelines — retrieval-augmented generation for internal knowledge and workflow automation Background in enterprise tooling — rapid prototyping tools (Vercel/Railway), identity automation (Entra ...

Language Models (LLMs), AI-assisted development tools, and modern AI frameworks. Has understanding of AI system architecture, prompt engineering, vector databases, RAG (Retrieval-Augmented Generation), and AI service integration and has experience evaluating Has knowledge of database systems and excellent problem-solving skills ...

experience across some or all of the following: Generative AI, Large Language Models (LLMs), AI assistants and AI agents. Retrieval Augmented Generation (RAG), enterprise search and knowledge management solutions. Solution, platform or enterprise architecture. Data architecture, integration, APIs and governance. Security, compliance, risk management ...

experience across some or all of the following: Generative AI, Large Language Models (LLMs), AI assistants and AI agents. Retrieval Augmented Generation (RAG), enterprise search and knowledge management solutions. Solution, platform or enterprise architecture. Data architecture, integration, APIs and governance. Security, compliance, risk management ...

advisor to client CxOs. Key Responsibilities Architecture and Solution Design: Architect enterprise AI solutions using frontier models (Claude, GPT, Gemini family) across agentic automation, RAG, copilots, and multi-modal applications. Pre-Sales and Discovery: Lead technical solutioning during pre-sales and discovery, translating client business challenges into scalable AI architectures … specifically on applied AI/ML or Generative AI. Hands-on GenAI Depth: Proven experience architecting solutions with frontier modelsincluding prompt engineering, fine-tuning, RAG, and agentic/multi-agent system design. LLMOps/MLOps Grounding: Expertise in evaluation frameworks, observability, cost and latency optimization, guardrails, and safe deployment ...

advisor to client CxOs. Key Responsibilities Architecture and Solution Design: Architect enterprise AI solutions using frontier models (Claude, GPT, Gemini family) across agentic automation, RAG, copilots, and multi-modal applications. Pre-Sales and Discovery: Lead technical solutioning during pre-sales and discovery, translating client business challenges into scalable AI architectures … specifically on applied AI/ML or Generative AI. Hands-on GenAI Depth: Proven experience architecting solutions with frontier modelsincluding prompt engineering, fine-tuning, RAG, and agentic/multi-agent system design. LLMOps/MLOps Grounding: Expertise in evaluation frameworks, observability, cost and latency optimization, guardrails, and safe deployment ...

with Azure OpenAI, Azure AI Document Intelligence or comparable cloud-based AI and document-processing services. Experience designing solutions involving generative AI, retrieval-augmented generation or AI agents. Knowledge of secure prompt design, model governance, data privacy and responsible AI controls. Experience with ...