20 of 20 Permanent 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 ...

across the organisation. Build AI assistants, copilots, intelligent automation tools and knowledge management solutions. Develop Retrieval-Augmented Generation (RAG) solutions using internal business data. Create scalable AI architectures and support the ongoing management of AI platforms and infrastructure. Integrate AI solutions with business-critical … experience building and deploying AI or Machine Learning solutions. Strong Python development skills. Experience working with REST APIs and systems integration. Knowledge of LLMs, RAG architectures, vector databases and modern AI frameworks. Experience with AWS cloud services and infrastructure. Familiarity with Docker and Kubernetes. Experience integrating AI solutions with Microsoft ...

helping shape its long-term AI strategy Designing and building AI applications, assistants and workflow automation tools Creating Retrieval Augmented Generation (RAG) solutions using internal business data Working with stakeholders to identify opportunities where AI can reduce complexity and improve efficiency Designing scalable … operations, warranty and aftersales Current technologies include: Python SQL OpenAI & Anthropic APIs N8N Copilot Studio PostgreSQL AWS Docker & Kubernetes Experience with AI agents, LLMs, RAG or agentic AI frameworks would be highly beneficial. About you We're open to different backgrounds. You might already be working as an AI Engineer ...

management SQL and data integration patterns (batch/stream), data quality controls LLM/agent patterns: prompt/version management, tool/function calling, RAG, safety guardrails Observability and production support (logging/metrics/tracing, incident triage) Opening up a world of opportunity. Being open to different points ...

platform. This hands-on leadership role focuses on scalable engineering patterns, accelerating delivery, and mentoring development teams. You will design and oversee LLM, RAG, and Agentic AI integrations across Azure, AWS, and GCP, collaborate with architects to ensure secure, maintainable solutions, and set standards for observability, governance, and #J ...

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 ...

equivalentsPrompt engineering, context design and agentic workflow frameworksEvaluation harnesses, evaluation datasets and automated test suites for AI outputsRetrieval-augmented generation (RAG), vector search, embeddings and retrieval pipelines (desirable)LLM orchestration frameworks (LangChain, Semantic Kernel or equivalents – desirable)Technical and Professional ExpertiseStrong software engineering background … GitHub Copilot, Cursor or equivalents, with a practical view of where they help and where they mislead.Preferred Qualifications and ExperienceRetrieval-augmented generation, agentic workflows, tool use or fine-tuning in production settings.Experience with LLM APIs, orchestration frameworks, vector search, embeddings, retrieval pipelines, evaluation datasets ...

equivalentsPrompt engineering, context design and agentic workflow frameworksEvaluation harnesses, evaluation datasets and automated test suites for AI outputsRetrieval-augmented generation (RAG), vector search, embeddings and retrieval pipelines (desirable)LLM orchestration frameworks (LangChain, Semantic Kernel or equivalents – desirable)Technical and Professional ExpertiseStrong software engineering background … Copilot, Cursor or equivalents, with a practical view of where they help and where they mislead. Preferred Qualifications and ExperienceRetrieval-augmented generation, agentic workflows, tool use or fine-tuning in production settings. Experience with LLM APIs, orchestration frameworks, vector search, embeddings, retrieval pipelines, evaluation ...

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 ...

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 ...

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 ...

rather than hype to determine whether an AI approach is actually working. It would be advantageous if you have experience with: RAG (Retrieval-Augmented Generation) Agentic workflows and tool use Fine-tuning Vector search and embeddings Retrieval pipelines LLM orchestration frameworks such ...

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. #J ...

articulate the business problem behind the technology you're building. It would be advantageous if you also have experience with: RAG (Retrieval-Augmented Generation) Agentic workflows and tool use Vector databases/search and embeddings LLM APIs from multiple providers AI evaluation frameworks ...

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. Kubernetes, Docker, Microservices … REST APIs. 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. Candidate Profile Strong engineering and problem-solving skills. Product engineering mindset with ...

language model APIs across multiple providersAI coding tools: GitHub Copilot, Cursor or equivalentsPrompt engineering and context design patternsRetrieval-augmented generation (RAG), vector search, embeddings (desirable)Evaluation frameworks and automated quality pipelines for AI outputsTechnical and Professional ExpertiseSolid production experience with C# .NET, including working in established … results to improve quality.Daily fluency with AI coding tools such as GitHub Copilot, Cursor or equivalents.Preferred Qualifications and ExperienceRetrieval-augmented generation, agentic workflows, tool use, vector search or embeddings in production settings.Experience with LLM APIs across more than one provider, with a feel for their trade ...

language model APIs across multiple providersAI coding tools: GitHub Copilot, Cursor or equivalentsPrompt engineering and context design patternsRetrieval-augmented generation (RAG), vector search, embeddings (desirable)Evaluation frameworks and automated quality pipelines for AI outputsTechnical and Professional ExpertiseSolid production experience with C# .NET, including working in established … improve quality. Daily fluency with AI coding tools such as GitHub Copilot, Cursor or equivalents. Preferred Qualifications and ExperienceRetrieval-augmented generation, agentic workflows, tool use, vector search or embeddings in production settings. Experience with LLM APIs across more than one provider, with a feel for their ...

language model APIs across multiple providersAI coding tools: GitHub Copilot, Cursor or equivalentsPrompt engineering and context design patternsRetrieval-augmented generation (RAG), vector search, embeddings (desirable)Evaluation frameworks and automated quality pipelines for AI outputsTechnical and Professional ExpertiseSolid production experience with C# .NET, including working in established … improve quality. Daily fluency with AI coding tools such as GitHub Copilot, Cursor or equivalents. Preferred Qualifications and ExperienceRetrieval-augmented generation, agentic workflows, tool use, vector search or embeddings in production settings. Experience with LLM APIs across more than one provider, with a feel for their ...

with strong AI engineering and MLOps, and deliver production-grade LLM features (NLQ over telemetry, summarisation, incident triage, on-call copilots) using prompt engineering, RAG, evaluation, and guardrails; collaborate with DS/ML teams on model versioning, monitoring, retraining, and drift detection, including low-latency inference in streaming/serving … dashboardsApplied AI/ML engineering in production – building or integrating AI capabilities (e.g., anomaly detection, forecasting, features) including familiarity with LLM APIs, prompting/RAG/embeddings/vector DBs, and the model lifecycle/MLOps basicsFrontend engineering background JavaScript/TypeScript with React/Angular/Vue, building performant ...

with strong AI engineering and MLOps, and deliver production-grade LLM features (NLQ over telemetry, summarisation, incident triage, on-call copilots) using prompt engineering, RAG, evaluation, and guardrails; collaborate with DS/ML teams on model versioning, monitoring, retraining, and drift detection, including low-latency inference in streaming/serving … dashboardsApplied AI/ML engineering in production – building or integrating AI capabilities (e.g., anomaly detection, forecasting, features) including familiarity with LLM APIs, prompting/RAG/embeddings/vector DBs, and the model lifecycle/MLOps basicsFrontend engineering background JavaScript/TypeScript with React/Angular/Vue, building performant ...