601 to 625 of 889 Retrieval-Augmented Generation Jobs in England

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 ...

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 ...

initiatives across EMEA, delivering business value through AI-enabled solutions in operational domains. You will design, develop, and deploy enterprise-grade GenAI solutions using RAG, Prompt Engineering, Open-Source LLMs, LangChain, and related frameworks; lead programs and mentor teams while aligning with Capgemini's global AI4OPS strategy. #J-18808-Ljbffr ...

across a large enterprise. You will drive security architecture, enforce secure-by-design patterns and collaborate with engineering, cloud and security teams on LLMs, RAG, AI agents and data flows. The role is hybrid with bases in London or Reading. Visa sponsorship is available for candidates currently living ...

across a large enterprise. You will drive security architecture, enforce secure-by-design patterns and collaborate with engineering, cloud and security teams on LLMs, RAG, AI agents and data flows. The role is hybrid with bases in London or Reading. Visa sponsorship is available for candidates currently living ...

Commercial experience building production AI/ML applications Strong Python engineering skills Experience with modern LLMs and agentic AI systems Hands-on experience with RAG, vector databases, and LLM orchestration Experience deploying and operating production ML systems Comfort working across models, infrastructure, and product Условия: Equity options Opportunity to shape ...

agentic AI solutions. Strong understanding of Azure AI architecture, security, governance and best practice. Experience connecting internal/proprietary business data to LLMs, including RAG, grounding, model customisation and/or fine-tuning. The ability to operate as both a hands-on technical SME and trusted advisor. Previous experience training ...

product management experience, translating customer needs into actionable roadmaps Experience leading or coordinating technical teams Comfortable discussing modern AI/software technologies including LLMs, RAG and data pipelines Strong commercial, scope, budget and delivery management skills Comfortable operating autonomously in a fast-moving environment Excellent communication skills across technical, scientific ...

training or fine-tuning workflows, and modern ML frameworks (PyTorch, Hugging Face, or equivalent). Methodology/Protocols: Strong grounding in evaluation methodology, RAG and fine-tuning trade-offs, and production ML deployment patterns. Soft Skills: Confident and credible in front of senior technical stakeholders, comfortable with pushback in live ...

medical device software (e.g. IEC 62304, IEC 81001-5-1) AI fluent - You have strong technical knowledge of modern generative AI (e.g., LLMs, embeddings, RAG, fine-tuning, evaluation/monitoring) and have a rich understanding of how these systems work - and where they fail The interview process Intro chat with ...

evaluations in parallel, prioritize what moves deals, and your job description will change as the region grows Familiarity with the modern AI stack — LLMs, RAG, agents — and how customers evaluate it; you can scope a POC with clear success criteria Based in region with willingness to travel for customer engagements ...

Proven experience engineering technology solutions in complex environments and supporting production operations Applied AI/ML and GenAI experience with knowledge of established patterns (RAG, tool use, guardrails, evaluation, agents) and an ability to evaluate tools and approaches Experience of working in teams employing agile practices and using modern SDLC ...

Amazon Quick capabilities (AI-powered search, research agents, custom agents, workflows, knowledge management) and the AWS Gen AI platform (Amazon Bedrock, SageMaker, foundational models, RAG architectures) with ability to articulate technical capabilities in business value terms to executive audiences- Demonstrated experience leading enterprise change management initiatives involving AI/automation ...

Proven experience engineering technology solutions in complex environments and supporting production operations Applied AI/ML and GenAI experience with knowledge of established patterns (RAG, tool use, guardrails, evaluation, agents) and an ability to evaluate tools and approaches Experience of working in teams employing agile practices and using modern SDLC ...

coding tools (Claude Code, Devin, Cursor/Windsurf, Copilot) with informed views on where each earns its keep. LLM application engineering — agent orchestration, RAG, structured outputs, evals. Prior consultancy or client-facing delivery experience. Cloud and data infrastructure depth (GCP/AWS, Terraform, CI/CD, warehouses, ETL). Front ...

deep-dive research into model performance (both open-weight and closed) to identify the best tools for niche domains, optimising them through context engineering, RAG, and other inference-time techniques.What You Will DoArchitect Agentic Systems: Design and build agent architectures, the harnesses, tool-use protocols, and logic flows that allow ...

relevant stakeholdersSales Partnership: Collaborate with the sales team to drive account success through account planning, opportunity prioritization/qualification and pipeline generation strategy, while taking ownership of the technical aspects (including but not limited to technical discovery, demos, proof of value, presentations, sizing and documentation)Demand Generation … complementary technologies such as Apache Kafka and KubernetesModern Architecture Design:Design Patterns and Methodologies: Embrace best practices in microservices, DevOps, cloud, and securityCutting Edge RAG and AI Architectures: Help customers on their generative AI journey and working with industry leading partners in the spaceSales Techniques and Soft Skills:Effective Communication ...

relevant stakeholders Sales Partnership: Collaborate with the sales team to drive account success through account planning, opportunity prioritization/qualification and pipeline generation strategy, while taking ownership of the technical aspects (including but not limited to technical discovery, demos, proof of value, presentations, sizing and documentation) Demand Generation … such as Apache Kafka and Kubernetes Modern Architecture Design: Design Patterns and Methodologies: Embrace best practices in microservices, DevOps, cloud, and security Cutting Edge RAG and AI Architectures: Help customers on their generative AI journey and working with industry leading partners in the space Sales Techniques and Soft Skills: Effective ...

tools.Design and maintain high-quality AI context, including system instructions, prompt templates, examples, reference materials, retrieval sources, workflow guardrails, and output formats.Support RAG and document-intelligence workflows, including document ingestion, chunking, metadata design, retrieval quality, structured extraction, and validation with subject-matter experts.Evaluate AI outputs … recommended usage patterns clearly to both technical and non-technical stakeholders.Stay current on practical advancements in applied AI, context engineering, enterprise copilots, agentic workflows, RAG, and AI evaluation methods.QualificationsBachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Information Systems, Business Analytics, Economics, Mathematics, or a related technical ...

production in an enterprise context (beyond prototypes or sandbox experiments).AI architecture for experience platforms: system design for intelligent products and services, including RAG, GraphRAG, agent orchestration and proximity/relevance evaluation.Hands-on implementation with Vercel AI SDK, Vercel AI Cloud and agentic cloud patterns for production AI experiences.AI content … value analysis, observability, governance, testing and evaluation frameworks for production systems.Practical application of Generative AI to marketing and experience challenges; e.g. personalisation, content generation, campaign optimisation; with measurable production outcomes.Design Systems and Design to CodeDesign to Code workflow: advanced Design Systems thinking; experience bridging design intent through ...

production in an enterprise context (beyond prototypes or sandbox experiments). AI architecture for experience platforms: system design for intelligent products and services, including RAG, GraphRAG, agent orchestration and proximity/relevance evaluation. Hands‐on implementation with Vercel AI SDK, Vercel AI Cloud and agentic cloud patterns for production … analysis, observability, governance, testing and evaluation frameworks for production systems. Practical application of Generative AI to marketing and experience challenges; e.g. personalisation, content generation, campaign optimisation; with measurable production outcomes. Design Systems and Design to Code Design to Code workflow: advanced Design Systems thinking; experience bridging design intent ...

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 ...

SDET Engineer or Senior DeveloperHas breadth of experience across different tech-stacks, such as JavaScript/TypeScript, Java, C#, or Python, amongst othersExperienced in RAG Weaviate, OpenAI, Agents, MCP, Langchain, Prompt Engineering, Browser-Use, and DjangoExperienced building AI Tools, Agents and Agentic workflows (LangGraph, CrwAI)Hands-on Product building experience … that cuts across the full SDLC - not just test automation and QE.Experienced in building RAG system with a nice-to-have on fine-tuning LLMsCleaning/augmenting dataComfortable with IAS (Infra As Code) eg. giving a high-level specFully understands test automation (across platforms and functional/non-functional ...

efficiency ; shorter cycle times, less manual effort, higher accuracy, lower cost-to-serve. Engineer the agent core: apply prompt engineering, context engineering, prompt caching, RAG/context-graph retrieval, memory, tool/function calling, MCP integration and multi-agent orchestration. Integrate to standards: connect agents into client ecosystems … Agents SDK, AWS Bedrock AgentCore or Microsoft Agent Framework/Semantic Kernel). Solid command of agent engineering: prompt engineering, context engineering, prompt caching, RAG/context graphs, tool/function calling, MCP, and multi-agent orchestration. Eval-driven development: designing evaluation harnesses and measuring agent quality, safety and reliability. ...

working with technologies including Azure Databricks, Azure Machine Learning, Azure AI Foundry, LangChain, LangGraph and Python, building enterprise AI applications using RAG, vector databases, prompt orchestration and tool‐calling patterns. You’ll need experience with: Deploying LLMs and machine learning models into Azure enterprise environments. Azure Databricks, Azure Machine Learning … ideally Azure AI Foundry. Building production AI applications using LangChain, LangGraph and Python. MLOps/LLMOps including deployment, monitoring, governance and lifecycle management. RAG, vector databases, prompt engineering and tool‐calling patterns. Building end-to-end AI/ML pipelines from data preparation through to production. Working with technical ...