651 to 675 of 878 Retrieval-Augmented Generation Jobs in the UK

event-driven architectures, including Kafka Agentic AI and ML Commercial and open-source LLMs integrated into agentic workflows Tooling for agent orchestration, retrieval-augmented generation, vector storage, and evaluation Strong schema, validation, and state management practices AI-assisted developmen t Fluency with AI-assisted ...

outcomes in partnership with cross-functional teams Preferred Qualifications, Capabilities, and Skills Experience with NLP and generative AI, including large language models, retrieval-augmented generation, tool and function calling, agentic workflows, multi-agent orchestration, and related evaluation and safety patterns Familiarity with agentic building ...

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 ...

extract patterns from one‐off work and turn them into reusable assets for others. Applied AI and Agentic Systems Experience building with LLMs, agents, RAG, tool use, workflow orchestration, or automation systems. Ability to reason about multi‐agent, human‐in‐the‐loop, and enterprise integration patterns. Comfort debugging agent behavior ...

Manage a pipeline of enterprise accounts and hit quarterly ACV targets (€50K–€500K+ deals, land-and-expand) Run technical sales conversations on agent and RAG architectures, retrieval, evaluation, and pipeline orchestration, and handle security and compliance questions directly (SOC 2, EU AI Act, on-prem/VPC) Build ...

business value in a secure and scalable manner. Collaborate with senior stakeholders, design end-to-end Gen AI architectures, and develop reusable patterns for RAG, agent orchestration and knowledge retrieval. #J-18808-Ljbffr ...

govern end-to-end GenAI architectures on Azure for insurance workflows, including underwriting and claims. You will lead the implementation, drive agentic AI and RAG strategies, ensure security, cost efficiency, and regulatory compliance across enterprise systems while mentoring .NET and React teams. The role offers flexible Hybrid working ...

teams discover insights, automate workflows, and drive faster decisions. You will shape product strategy, roadmaps, and success metrics for analytics-enabled initiatives, leveraging LLMs, RAG, and conversational interfaces to deliver value while #J-18808-Ljbffr ...

Applied AI Engineer to design and productionise AI-powered features within a modern .NET platform in Nottingham. The role involves integrating LLMs, building RAG pipelines and APIs, and delivering scalable backend services in a hybrid setup with two days in the office. You will work with Product, Engineering and Data ...

measure business impact, manage technical risk, and mentor fellow engineers while serving as a trusted client technical contact. You will work across GenAI/RAG workflows, deploy on cloud platforms, and contribute to reusable accelerators and best practices to #J-18808-Ljbffr ...

Delivery Tech Lead to design and deliver enterprise-scale AI systems in a production environment. You will work on CCaaS/AI integration, RAG pipelines, and multi-agent architectures, collaborating with cloud platforms and strategy leads to drive innovation. The role requires hands-on engineering, strong Python experience ...

need someone with the "Phase 0 first" mindset- starting with deterministic deduplication before touching LLMs, building evaluation harnesses before declaring victory, understanding why RAG isn't always the answer for structured knowledge. This is about engineering discipline and pragmatism over hype. Experience at companies dealing with similar challenges is valuable ...

native by design. Cursor, Claude, the lot. You'll design agentic workflows (automated reviews, LLM-assisted QA) and contribute directly to AI-powered features - RAG, function calling, evaluation frameworks. You'll also help pick the tooling and set the patterns others follow. Beyond the code, you'll own delivery across ...

systems using React, C++, and/or RubyExperience building & maintaining open source software and/or libraries for developersKnowledge and experience in generative AI, RAG, the fine-tuning process and its application to creative productsAt Figma, one of our values is Grow as you go. We believe in hiring smart ...

Badger, our purpose is to make a positive impact on the world around us. Our mission is to transform blue‐chips into next‐generation digital product organisations. We believe in creating opportunities for all. We benefit from diversity. That said, we’re far from perfect – we want … LLMs and SLMs, agentic automation, Human in the Loop and MLOps. Data Architecture Design : You know when to use RAG (Retrieval‐Augmented Generation), Serverless, DWH models, Data Mesh, Data Fabric, Medallion and Data Products. Hands on mindset : Fluent in the language of data, whether ...

pathway to the Forward Deployed Engineerprogramme. Key Responsibilities Architect and govern production-grade agentic systems at enterprise scale: multi-agent orchestration across complex environments, RAG pipelines, policy-based routing, memory management, andprogramme-level lifecycle observability Define RAG pipeline standards across engagements:establishchunking and embedding strategies, set quality benchmarks, and ensure … production depth, not tutorial level Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management,latencyand cost tradeoffs RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context engineering LLMOpsfundamentals: eval harness design, prompt versioning, and production observability Cloud-native engineering maturity: Kubernetes, Docker ...

pathway to the Forward Deployed Engineerprogramme. Key Responsibilities Architect and govern production-grade agentic systems at enterprise scale: multi-agent orchestration across complex environments, RAG pipelines, policy-based routing, memory management, andprogramme-level lifecycle observability Define RAG pipeline standards across engagements:establishchunking and embedding strategies, set quality benchmarks, and ensure … production depth, not tutorial level Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management,latencyand cost tradeoffs RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context engineering LLMOpsfundamentals: eval harness design, prompt versioning, and production observability Cloud-native engineering maturity: Kubernetes, Docker ...

direct pathway to the Forward Deployed Engineer programme. Key Responsibilities Design and build production-grade agentic systems end-to-end: multi-agent orchestration, RAG pipelines, policy-based routing, tool invocation, memory management, and lifecycle observability Build and own RAG pipelines: embeddings, chunking strategy, vector search, context window engineering and tuning … depth, not tutorial level Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management, latency and cost tradeoffs RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context engineering LLMOps fundamentals: eval harness design, prompt versioning, and production observability Cloud-native engineering maturity: Kubernetes ...

direct pathway to the Forward Deployed Engineerprogramme. Key Responsibilities Design and build production-grade agentic systems end-to-end: multi-agent orchestration, RAG pipelines, policy-based routing, tool invocation, memory management, and lifecycle observability Build and own RAG pipelines: embeddings, chunking strategy, vector search, context window engineering and tuning against … production depth, not tutorial level Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management,latencyand cost tradeoffs RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context engineering LLMOpsfundamentals: eval harness design, prompt versioning, and production observability Cloud-native engineering maturity: Kubernetes, Docker ...

direct pathway to the Forward Deployed Engineerprogramme. Key Responsibilities Design and build production-grade agentic systems end-to-end: multi-agent orchestration, RAG pipelines, policy-based routing, tool invocation, memory management, and lifecycle observability Build and own RAG pipelines: embeddings, chunking strategy, vector search, context window engineering and tuning against … production depth, not tutorial level Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management,latencyand cost tradeoffs RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context engineering LLMOpsfundamentals: eval harness design, prompt versioning, and production observability Cloud-native engineering maturity: Kubernetes, Docker ...

predictive insights, document intelligence, fraud, compliance or customer‐service workflows. Familiarity with modern AI and GenAI development approaches, including LLMs, retrieval‐augmented generation, prompt design, agent orchestration and model governance. Awareness of AI‐enabled SDLC tools and practices such as requirements generation ...

build and deploy enterprise-grade AI solutions across an Enterprise Knowledge Base (EKB). This is a hands-on role focused on GenAI, LLMs, RAG and Agentic AI, taking solutions from prototype through to production. You'll work across AI, data and engineering teams to build intelligent retrieval, knowledge discovery and automation capabilities. Key Responsibilities Build production-grade GenAI, LLM, RAG and Agentic AI solutions Develop AI Agents and Multi-Agent Systems Work with LangChain, LangGraph and MCP Build intelligent retrieval using Embeddings, Vector Databases and Semantic Search Apply Prompt & Context Engineering Integrate ...

squad's needs evolve. Requirements Engineering experience: 3+ years in software engineering, with hands‐on experience building LLM‐powered applications in production (RAG, tool‐augmented agents or agentic workflows). Education: BS in Engineering, Computer Science, or equivalent. Ways of working: Comfortable working autonomously within a defined problem … doesn't make sense, and delivering in short, time‐boxed cycles where validated outcomes matter more than perfect solutions. Agentic AI/LLMs: Building RAG pipelines, tool‐augmented agents and agentic workflows; familiar with prompt engineering, context management, evaluation and observability. Agent fundamentals: Understands how agents use memory ...

Engineer, you will build user-facing applications and backend services that integrate with Model Context Protocol (MCP) servers, LLM-based applications, RAG-based retrieval systems, and agent orchestration platforms. You will work closely with AI developers, platform engineers, product teams, and other stakeholders to deliver scalable, secure, observable … tools, skills, APIs, and UI components for use across AI applications and agent runtimes. Integrate AI-powered user interfaces with backend APIs, MCP servers, RAG systems, and agent workflows. Implement RBAC policies governing which users, agents, clients, and applications can invoke specific tools or services. Develop agent workflows that support ...

/CD pipelines Debugging & problem-solving skills with strong ownership across the SDLC.Generative AI & ML Requirements Experience building agents Vector databases and RAG patterns (indexing, chunking, embeddings, retrieval). LLM integration (OpenAI, Azure OpenAI, Anthropic, or open source models) with prompt design, safety/guardrails, and cost/… perf tuning. Nice to have - Highly desirable: experience with AgentCore, AWS Neptune, Amazon API gateway- Familiarity with ML fundamentals relevant to content generation (embeddings, tokenization, evaluation, fine tuning/LoRA, prompt+retrieval evaluation)- Frontend development with React (TypeScript preferred) to build the application's UI (state management, routing ...