251 to 275 of 418 Retrieval-Augmented Generation Jobs in the UK

modern data stacks, and can reason about system design. Practical experience building applications on LLMs (OpenAI, Anthropic, open-source), including prompt engineering and RAG architectures. Experience training, deploying or productionising ML models, and measuring whether they actually work. Ability to translate a vague business problem into a technical solution ...

systems, core banking platforms and the integration challenges of deploying AI within regulated market systems.* Strong command of modern AI capabilities: LLMs, agentic frameworks, RAG, ML inference, and MLOps, sufficient to lead technical teams and challenge engineering decisions.* Demonstrated ability to drive AI adoption at scale, navigating organisational complexity, change ...

inform modelling and business decisions. Build, tune, andoptimisemachine learning, deep learning, and generative AI models,leveragingboth established andcutting-edgetechniques. Designand buildLLM-powered systems— RAG pipelines, prompt and context engineering, fine-tuning, structured outputs, function calling, context-window management,and secure model integration;selectingappropriately between standard and reasoning (test-time-compute … Kubernetes), event-driven or microservices architectures, and distributed data processing at scale. Experience with embedding pipelines, vector search, and semantic retrieval for RAG systems. Knowledge of LLM evaluation techniques including structured evals frameworks, hallucination mitigation, benchmarking, and human-in-the-loop evaluation. Understanding of AI safety, responsible ...

have delivered AI solutions within it. Typical projects include: Building AI agents and autonomous workflows Developing LLM-powered applications and copilots Designing enterprise RAG and knowledge retrieval platforms Production deployment of Generative AI solutions AI integration into enterprise products and business platforms Scaling AI systems across cloud environments … ideally have experience with: Python OpenAI, Anthropic, Gemini, Llama or similar LLMs LangChain, LangGraph, Semantic Kernel, CrewAI, AutoGen or equivalent frameworks RAG, vector databases and embeddings Azure AI, AWS AI or Google Vertex AI MLOps, CI/CD and cloud-native deployment Docker, Kubernetes and modern software engineering practices What ...

covers the full AI/ML engineering lifecycle, from discovery to deployment and monitoring. Responsibilities include Designing and implementing agentic systems using techniques spanning RAG, grounding, prompt engineering, and orchestration on a GCP‐first stack. Building and maintaining production ML pipelines and services for non‐GenAI use cases (e.g. recommender … Terraform), experiment tracking, model registry, and monitoring. Proven experience deploying and operating ML systems in production (batch and real‐time). Familiarity with RAG architectures, prompt engineering, guardrails and evaluation techniques. Previous experience developing agentic capabilities, such as agent skills, MCP servers, tool usage. Strong grasp of security, privacy ...

agent orchestration, tool use, skills use, and memory systems — and work hands‐on with foundation models through fine‐tuning, retrieval‐augmented generation (RAG), and custom model integration. A part of your work will also involve engineering the AI context layer that makes these systems … faithfulness, translating findings into design improvements Architect and implement foundation model integrations — selecting the right models, invocation patterns, and customization approaches (fine‐tuning, RAG, custom integration) based on capability, cost, and performance trade‐offs Design and build model adaptation and fine‐tuning pipelines, applying working knowledge of transformer‐based architectures ...

Studio. Experience designing and building LLM agents or AI-powered workflows using Azure OpenAI or similar technologies Knowledge of Retrieval Augmented Generation (RAG), vector search or agent orchestration frameworks would be advantageous If this sounds like a good fit, apply now as interviews commence ...

Linux-based environments and proficient in managing containerized workloads and automated CI/CD pipelines. Advanced RAG: Experience building production-ready Retrieval-Augmented Generation systems, including vector database management and semantic search optimization. Preferred Qualifications Experience in the insurance or financial services sector. Deep ...

shape strategy and drive delivery within a large, complex organisation Knowledge of modern AI approaches, including areas such as Retrieval Augmented Generation (RAG), prompt engineering, observability and evaluation Experience in cyber security, fraud analytics, behavioural analytics or threat detection would be particularly valuable This ...

shape strategy and drive delivery within a large, complex organisation Knowledge of modern AI approaches, including areas such as Retrieval Augmented Generation (RAG), prompt engineering, observability and evaluation Experience in cyber security, fraud analytics, behavioural analytics or threat detection would be particularly valuable This ...

shape strategy and drive delivery within a large, complex organisation Knowledge of modern AI approaches, including areas such as Retrieval Augmented Generation (RAG), prompt engineering, observability and evaluation Experience in cyber security, fraud analytics, behavioural analytics or threat detection would be particularly valuable. This ...

mapping tools. Familiarity with Microsoft 365 cloud ecosystem, Azure integrations, document management systems and virtual data rooms. Exposure to generative AI integration, prompt engineering, RAG architecture and LLM orchestration frameworks. Understanding of security architecture principles and legal‐sector information governance. Legal & Commercial Skills Proven experience in a law firm, ALSP ...

work is genuinely varied: one week you’re designing a data API, the next you’re improving retrieval quality in a RAG pipeline, the next you’re working with infrastructure to get a service into production. We’re looking for someone who finds that energising rather than uncomfortable. ...

work is genuinely varied: one week you're designing a data API, the next you're improving retrieval quality in a RAG pipeline, the next you're working with infrastructure to get a service into production. We're looking for someone who finds that energising rather than uncomfortable. ...

egress controls). Strong understanding of AI/LLM capabilities and risks across the lifecycle (model onboarding/ingestion, retrieval/RAG patterns, model serving) and associated control points (traceability, access, data handling), including assessing control design and operational effectiveness in fast‐changing environments. Experience with agentic ...

fine-tuning and RAG. Design and implement the context layer-spanning knowledge graphs, vector search, and semantic retrieval-to create reliable, grounded RAG pipelines. Architect foundation model adaptation strategies, including dynamic, cost-and-performance-aware model routing and selection. Design, implement, and prototype high-throughput, low-latency inferencing ...

egress controls). Strong understanding of AI/LLM capabilities and risks across the lifecycle (model onboarding/ingestion, retrieval/RAG patterns, model serving) and associated control points (traceability, access, data handling), including assessing control design and operational effectiveness in fast-changing environments. overseeing "Claude Code"-style ...

collaborate with product teams to deliver reliable AI tooling. The role requires strong Python, expertise with LLM frameworks, FastAPI, and Kubernetes, plus experience with RAG and tool orchestration in an enterprise setting. #J-18808-Ljbffr ...

working closely with senior stakeholders. What you'll need: Experience delivering AI/Generative AI solutions in a commercial environment Strong knowledge of LLMs, RAG and modern AI architectures Python and cloud platform experience (Azure preferred) Experience integrating AI solutions into enterprise applications Ability to explain complex technical decisions ...

experience in APIs, integrations and distributed systems Proven experience designing, building and delivering solutions into production environments Experience with AI architecture and patterns, including RAG, tool calling and multi‐agent orchestration Experience building or deploying AI/LLM‐powered solutions and workflows Full‐stack software engineering skills, particularly Python, APIs ...

customer‐facing team in a cloud computing environment. Experience developing AI/Generative AI solutions utilizing AI tools and designing multi‐agent workflows and RAG systems. Experience in people management. Preferred qualifications Master’s degree or PhD in AI, Computer Science, or a related technical field. Experience in architecting ...

launch and architecting AI systems on cloud platforms (e.g., GCP). Experience building pipelines for structured and unstructured data using both vector databases and RAG-like architectures to power enterprise AI solutions. Experience leading technical discovery sessions with customers. Active, or the ability to obtain, a Security Check ...

ship Experience building or deploying AI/LLM systems in production Familiarity with tools that power today's AI agents: eval frameworks, agent tooling, RAG pipelines, and prompt engineering Prior experience with React, TypeScript, and/or Go Previous roles where you interfaced with customers or led technical projects with ...

experience with software development using Python or similar coding languages. Experience building pipelines for structured and unstructured data using both vector databases and RAG-like architectures to power enterprise AI solutions. Experience taking production-grade AI-driven solutions from conception to launch, and architecting AI systems on cloud platforms (e.g. ...

Have Passion for or experience in e-learning projects is a strong bonus Hands-on experience building AI-powered features (e.g. LLM integrations, RAG, AI-assisted user experiences) is a strong bonus Experience running and monitoring AI products in production: evals, output quality tracking, and observability specific to AI/ ...