101 to 125 of 225 Retrieval-Augmented Generation Jobs in the UK

Balancing cost, performance, and reliability Applied AI Working with Azure OpenAI APIs beyond prompt demos Retrieval-augmented generation (RAG) Embeddings, vector stores, and evaluation Guardrails, rate limiting, and failure handling Human-in-the-loop workflows Integration & automation Integrating AI into existing enterprise systems Automating ...

create high-quality pre-trained datasets, fine-tuned industry-specific Large Language Models(LLMs), and Retrieval-Augmented Generation (RAG) pipelines supported by vector databases. Our innovations can reduce Generative Artificial Intelligence(Gen AI) costs by up to 80% and bring Gen AI solutions ...

. Expertise in modern machine learning frameworks (scikit-learn, XGBoost, PyTorch, TensorFlow). Demonstrable experience delivering end-to-end ML and GenAI solutions (including RAG, LLMs, embeddings and vector databases). Strong knowledge of MLOps tooling and practices (MLflow, CI/CD, containerisation, monitoring). Experience leading, mentoring and developing ...

subject matter expert, you will mentor a high-performing team, manage cross-functional integrations, and stay at the bleeding edge of AI (RAG, MCP, and SageMaker) to keep our projects ahead of the curve. What You'll Do Architect Decision Systems: Design innovative AI/ML models that … Science & Machine Learning. Cloud Expertise: Intermediate+ proficiency in Amazon SageMaker . Modern AI: Familiarity with Retrieval-Augmented Generation (RAG) and Model Context Protocol (MCP) . Engineering Rigor: Solid understanding of the Software Development Life Cycle (SDLC) . Please let me know if you would ...

Engineer will be responsible for developing secure, scalable, and high-performing AI solutions, including document intelligence workflows, generative AI applications, and retrieval-augmented systems. This includes working on model orchestration, prompt engineering, document extraction pipelines, and system integrations with enterprise platforms such … intelligent document processing pipelines using Azure AI Doc Intelligence for tasks such as classification, extraction, OCR, and automated document workflows. Implement RAG (Retrieval-Augmented Generation) architectures using Azure Search or vector databases. Integrate AI capabilities with enterprise systems through REST APIs, Azure Functions, Logic ...

following skills and experience: Strong Python and SQL experience, ideally with Databricks Experience with ML and LLM tools (e.g., scikit learn, XGBoost, PyTorch, TensorFlow, RAG, APIs) Comfortable with data engineering basics and MLOps practices Experience working with cloud platforms such as Azure, AWS, or GCP Benefits The successful Senior ...

pairs, decision trees, and intent-based chunks* Apply consistent taxonomy, tagging, and metadata to support retrieval-augmented generation (RAG) and AI consumption* Align knowledge with chatbot/model requirements, design prompt-ready content, analyze performance, and refine based on real user queries* Collaborate with ...

LangChain or Semantic Kernel. Solid understanding of ML concepts (supervised/unsupervised learning, transformers, CNNs/RNNs, model evaluation). Experience with prompt engineering, RAG pipelines, and model fine-tuning. The judgement to identify where AI adds value — and where it doesn’t. Experience deploying and operationalising LLMs (exposure ...

using Microsoft Copilot tools to automate business workflows (e.g., HR queries, IT support, operational data retrieval). Develop custom RAG (Retrieval-Augmented Generation) solutions within Databricks to allow LLMs to reason over proprietary SMS documents and data. Integrate AI agents with enterprise … Experience with defining metrics layers (e.g., DBX semantic layer or Databricks Genie) is essential. Practical experience with Large Language Models (LLMs), Prompt Engineering, and RAG architectures. Proficient in Python (for data manipulation and API interaction) and SQL (for data modelling). Experience working with REST APIs and connecting disparate business ...

Communicate complex concepts to all stakeholders. At least 3 years of experience in AI solution engineering. Large Language Models experience including prompt engineering and RAG implementations. Expert data analytics, MLOps practices and API development. Desirable knowledge in Docker and Kubernetes ...

microservices or serverless architectures Practical exposure to machine learning plus generative AI models including LLMs SLMs vision models Familiarity with AI frameworks such as RAG LangChain LlamaIndex plus agentic frameworks Hands-on cloud experience within Azure or AWS plus CI/CD tooling and Git Desirable Skills ...

Strong Python development experience within modern data science environments Proven track record building production LLM applications using commercial APIs Deep experience with prompt engineering, RAG architectures and function calling Production experience with cloud platforms including AWS, GCP or Azure Knowledge of containerisation and CI/CD practices Advanced AI Capability ...

Architect & Engineer AI Systems: You won’t just call an API; you will build robust processing architectures that leverage off-the-shelf LLMs, RAG pipelines, and context engineering to solve hard problems. You will benchmark these systems rigorously to ensure they actually work, not just "feel" smart. Build the Backend ...

least 2 AI domains: NLP, NLG, Computer Vision, Machine Learning, GenAI etc. Hands-on experience with fine-tuning/training LLMs and complex Graph RAG solutions (production environment). Technical Skills: Extensive knowledge of cloud services and tools (Azure & Databricks preferred), ), including Azure Machine Learning, Vertex AI, AI Search, Databricks ...

development is a plus Key responsibilities Design, build and deploy distributed systems and AI agents that leverage LLMs and retrieval‐augmented generation (RAG) to provide contextual, reasoning‐based capabilities. Develop cloud‐native microservices and data pipelines to deliver high‐performance inference and contextual retrieval … software development experience building scalable backend systems; strong knowledge of distributed system architecture, concurrency and microservices. Experience developing LLM‐powered applications, including prompt engineering, RAG and agentic frameworks. Proficiency in one or more programming languages such as Python, Java, Go or Rust; experience with cloud platforms (AWS, Azure ...

Proven experience as an AI or Machine Learning Engineer with end-to-end model ownership Strong expertise in NLP and LLMs (transformers, fine-tuning, RAG, agents) Experience translating research and experimentation into production systems Solid understanding of MLOps, including CI/CD, monitoring and model lifecycle management Hands-on experience ...

Proven experience as an AI or Machine Learning Engineer with end-to-end model ownership Strong expertise in NLP and LLMs (transformers, fine-tuning, RAG, agents) Experience translating research and experimentation into production systems Solid understanding of MLOps, including CI/CD, monitoring and model lifecycle management Hands-on experience ...

Proven experience as an AI or Machine Learning Engineer with end-to-end model ownership Strong expertise in NLP and LLMs (transformers, fine-tuning, RAG, agents) Experience translating research and experimentation into production systems Solid understanding of MLOps, including CI/CD, monitoring and model lifecycle management Hands-on experience ...

health monitoring, and production readiness requirements. • GenAI Development Expertise: Demonstrated experience building production features using LLMs and GenAI technologies, including prompt engineering, model integration, RAG patterns, agentic workflows, and guardrails. Ability to define and measure quality, accuracy, and observability metrics for GenAI features in production. • Machine Learning & Data Science Knowledge ...

shape how GenAI and ML are used across the platform. Tech stack: Python PyTorch or TensorFlow scikit-learn Spark Kafka Databricks LangChain and RAG-based GenAI systems Cloud-native Azure environment Important Details: Fully remote across the UK Up to £80,000 - £100,000 (DOE) 2-3 stage interview process ...

experience and API development Experience with data pipelines and vector databases Knowledge of SRE, DevOps, and platform engineering practices Exposure to AI systems, LLMs, RAG, or agent architectures Experience working with UK government or secure environments You'll work alongside expert architects and engineers to deliver complex, high-impact solutions ...

including AI and ML services Solid foundation in software engineering principles for scalable, production-grade systems Experience designing and delivering enterprise AI solutions including RAG-based architectures using vector databases Proven experience delivering full-stack AI or ML systems within enterprise environments Strong understanding of advanced agent architectures, reasoning systems ...

including AI and ML services Solid foundation in software engineering principles for scalable, production-grade systems Experience designing and delivering enterprise AI solutions including RAG-based architectures using vector databases Proven experience delivering full-stack AI or ML systems within enterprise environments Strong understanding of advanced agent architectures, reasoning systems ...

handling both single-step LLM tasks and complex multi-step agentic workflows. As AI Engineer you will: Architect and implement the LLM, agentic and RAG modules that power the platform’s low-code environment. Build primitives, abstractions and orchestration components that allow clients to construct robust agentic workflows with deterministic ...

model training to inference APIs and monitoring. Integrate and scale LLMs & generative AI within product workflows, including fine-tuning, prompt engineering, RAG (retrieval-augmented generation), and agentic systems. Collaborate with product, backend, and frontend teams to deliver impactful AI-driven experiences. Take ownership ...