11 of 11 Retrieval-Augmented Generation Jobs in Sheffield

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 ...

prompt engineering combined with traditional software/automation engineering. Hands-on expertise utilizing GitHub Copilot, Microsoft Copilot, and Claude Code for code generation, review, and refactoring. Deep proficiency in PowerShell scripting, enterprise automation, and system administration. Strong database background, specifically with SQL Server. Programming proficiency in at least … with Azure AI, OpenAI APIs, or Anthropic APIs. Solid understanding of CI/CD, DevOps, and enterprise automation platforms. Familiarity with RAG (Retrieval-Augmented Generation) architectures, vector databases, and knowledge systems. Previous experience deploying AI solutions within large-scale or regulated enterprise environments. ...

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 ...

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 ...

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 ...