6 of 6 Retrieval-Augmented Generation Jobs in South 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 ...

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 ...

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 ...