13 of 13 Retrieval-Augmented Generation Jobs in the Midlands

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 ...

core data science libraries such as pandas, scikit-learn, LangChain, etc. Experience with LLMs, e.g. foundation model APIs, prompt engineering, retrieval augmented generation Substantial depth and breadth across state-of-the-art AI/ML techniques such as Generative AI, NLP, Deep Learning, etc. ...

EventBridge, SQS, Step Functions Databases: DynamoDB, PostgreSQL, Aurora, MongoDB Frontend: React, TypeScript, MUI DevOps: AWS CDK, Terraform, Docker, GitLab CI, CodePipeline AI: Amazon Bedrock, RAG, LLM integrations Skills Required Commercial software engineering experience Strong AWS cloud/serverless experience Strong backend development skills in TypeScript, Python, or Go CI/ ...

software engineering best practices. Support end-to-end MLOps processes including deployment, monitoring and model validation. Explore and implement generative AI capabilities including LLMs, RAG pipelines, and intelligent workflow automation. Key Skills: Commercial experience in Machine Learning Engineering, Applied AI, or related software engineering roles. Strong programming skills in Python … pipelines, and scalable deployment practices. Understanding of MLOps concepts including experiment tracking, model monitoring, and reproducibility. Exposure to Generative AI technologies including LLMs, RAG, or prompt engineering is a plus. Strong communication skills and ability to work effectively within cross-functional agile teams. ...

leading and developing engineering teams, including growing high-performing teams and improving delivery effectiveness. Strong understanding of modern AI concepts and architectures, including LLMs, RAG, and fine-tuning, with the ability to assess AI-driven technical proposals independently. Genuine interest in AI and emerging technologies, with hands-on experimentation using ...

leading and developing engineering teams, including growing high-performing teams and improving delivery effectiveness. Strong understanding of modern AI concepts and architectures, including LLMs, RAG, and fine-tuning, with the ability to assess AI-driven technical proposals independently. Genuine interest in AI and emerging technologies, with hands-on experimentation using ...

operational software — turning fragmented operational data into structured, trusted intelligence. We're focused on difficult, practical problems: Text-to-SQL against real enterprise schemas RAG over messy operational data Screen recorded workflow mapping Workflow and knowledge tooling that operational teams will actually adopt The platform is being developed alongside … t. Tech you'll touch Laravel · Livewire · Alpine · Tailwind · Python · SQL Server (T-SQL) · PostgreSQL · Power BI/DAX · Docker · GitHub · LLM integrations · RAG · embeddings · Claude Code & Cursor daily Logistics Location: Fully remote, UK Hours: Full-time Type: Contract-to-perm. Outside IR35. Salary: £70,000–£80,000 on conversion ...