101 to 125 of 341 Retrieval-Augmented Generation Jobs in the UK excluding London

transfer learning, text and token classification. Working knowledge of large language models, including prompting and retrieval-augmented generation (RAG).Experience or coursework in Deep Learning frameworks like PyTorch or TensorFlow. Strong problem-solving skills and a curiosity for learning new technologies. Good verbal ...

both customers and organisations. Modern AI architecture expertise. Deep understanding of machine learning, LLMs, retrieval-augmented generation (RAG), agentic systems and the architectural patterns used to deploy AI solutions at enterprise scale. Engineering and cloud foundations. Strong understanding of software engineering, data architecture ...

help build the data foundations that power enterprise search, knowledge discovery, AI applications and Retrieval-Augmented Generation (RAG). What you'll be doing Design and build scalable ETL/ELT data pipelines across multiple enterprise data sources Develop data solutions using BigQuery, Google ...

Familiarity with CI/CD pipelines and software development lifecycles Experience with prompt engineering and retrieval-augmented generation (RAG) based architecture Ability to communicate with clarity and credibility across senior engineers, stakeholders, and product partners Preferred Qualifications, Capabilities, and Skills Experience building agentic tools ...

Integrate AI/ML components into applications and operational workflows Design and implement Retrieval-Augmented Generation (RAG) solutions Evaluate LLM performance, identify failure modes, and implement mitigation strategies Test, debug, and improve non-deterministic AI systems Balance AI approaches with deterministic or statistical methods … performance Ability to debug complex, non-deterministic behaviours Strong communication skills to explain AI trade-offs and risks Bonus Skills Hands-on experience with RAG architectures and LLM tooling Experience working in regulated or high-assurance environments Familiarity with AI safety, risk, and governance considerations Background in software engineering (Python ...

from the ground up, with significant scope for ownership, innovation and career development. Key Responsibilities Design and deliver AI solutions using Microsoft Azure, including RAG, data classification, forecasting and anomaly detection. Identify opportunities to introduce AI into existing business processes and systems. Integrate AI solutions with Microsoft 365, SharePoint, Teams … take AI projects from prototype through to robust production deployment. Desirable Skills Experience with Retrieval-Augmented Generation (RAG). Knowledge of vector databases. Experience with AI frameworks such as Haystack, LlamaIndex or Semantic Kernel. Experience with forecasting, anomaly detection or data classification. Knowledge ...

coverageYour qualificationsEarly-career professional with proven commercial experience in: Troubleshooting Large Language Model (LLM) and Retrieval-Augmented Generation (RAG) applicationsRobust prompt and context engineeringData analysis (Excel, Anaplan or similar tools)Software development or quality assuranceLevel 2 or level 3 technical supportA degree in Mathematics ...

most cutting-edge defence and aerospace projects in the UK? If the prospect of developing software for projects such as a sixth-generation stealth fighter jet or a revolutionary Laser Directed Energy product excites you, then Leonardo would like to hear from you. Leonardo is seeking to recruit … fine-tuning large language models (LLMs). Experience building scalable pipelines, including APIs, vector databases, retrieval-augmented generation (RAG), and orchestration frameworks. Experience curating datasets, managing embeddings, and ensuring security, privacy, and responsible AI practices. Experience in prompt engineering, with the ability to design ...

most cutting-edge defence and aerospace projects in the UK? If the prospect of developing software for projects such as a sixth-generation stealth fighter jet or a revolutionary Laser Directed Energy product excites you, then Leonardo would like to hear from you. Leonardo is seeking to recruit … fine-tuning large language models (LLMs). Experience building scalable pipelines, including APIs, vector databases, retrieval-augmented generation (RAG), and orchestration frameworks. Experience curating datasets, managing embeddings, and ensuring security, privacy, and responsible AI practices. Experience in prompt engineering, with the ability to design ...

quality outcomes. Experience in one or more of the following would be beneficial: Semantic Kernel Retrieval-Augmented Generation (RAG) architectures Microsoft Fabric Docker Cloud-native services Managed services platforms Centerprise is a well-established UK technology provider with over 40 years of experience delivering ...

APIs and backend services that power enterprise customer integrations Owning the LLM application layer, including retrieval-augmented generation (RAG) workflows Improving model performance, reliability, evaluation frameworks, monitoring, and cost optimisation Working across the stack to deliver customer-facing features and platform capabilities Collaborating with … engineering expertise, ideally within API-first products Experience with Node.js and TypeScript, or a proven ability to transition successfully between technology stacks Understanding of RAG architectures, evaluation frameworks, guardrails, monitoring, and model optimisation Experience managing token usage, infrastructure costs, and scalability challenges in AI products Comfortable working autonomously ...

Apply clean code, automated testing, CI/CD, observability and other modern engineering principles Build and integrate AI-enabled features, including LLM-powered services, RAG and agent-based workflows where appropriate Use AI-assisted development tools such as GitHub Copilot, Claude Code, Cursor or similar to improve engineering productivity Develop … Desirable: Experience integrating AI into production systems (e.g. chat interfaces, automation, intelligent search) Familiarity with Retrieval-Augmented Generation (RAG), vector databases, or embeddings Experience with cloud platforms (AWS preferred) Awareness of emerging AI tooling (e.g. MCP, A2A, Threads or similar) Open-source contributions ...

platforms. Hands-on experience with AI orchestration and agent frameworks including LangChain, LangGraph, Semantic Kernel, LlamaIndex, CrewAI, and AutoGen. Strong knowledge of Prompt Engineering, RAG, Vector Databases (Pinecone, Weaviate, Chroma, FAISS, Azure AI Search), Model Fine-Tuning, LLMOps, and Secure AI practices. Enterprise Architecture & Security Proficiency in enterprise architecture frameworks ...

shipping AI-powered products end-to-end using LLM technologies. Owning and scaling the retrieval-augmented generation (RAG) and AI application layer. Developing secure, production-grade systems including guardrails, evaluation frameworks, monitoring, caching, and token cost optimisation. Working across the stack, including backend services ...

automation and optimisation. Design and build AI solutions using technologies such as LLMs, agents and retrieval-augmented generation (RAG). Create practical roadmaps for AI adoption, including prioritised implementation plans. Engage with business stakeholders to communicate opportunities, risks and recommendations. Develop, deploy and scale … delivering end-to-end AI solutions in commercial environments. Capability to work across strategy, architecture and hands-on engineering. Expertise with LLMs, AI agents, RAG frameworks and modern AI tooling. Experience assessing business processes and translating requirements into technical solutions. Strong stakeholder management and communication skills. Background working within smaller ...

multi‐modal large language models across text, image, audio, and video Design and implement agentic workflows using prompt engineering, Retrieval‐Augmented Generation (RAG), APIs, and enterprise data integrations Fine‐tune, optimize, and deploy large language and multi‐modal models with a focus on scalability … within agile delivery environments Bonus Points For Experience building AI agent frameworks or autonomous orchestration platforms Knowledge of vector databases, semantic search, and advanced RAG architectures Experience with cloud platforms such as AWS, Azure, or GCP for AI workloads Familiarity with financial services, banking, or regulated enterprise environments Exposure ...

across the organisation. Responsibilities Technical Leadership & Engineering Design Agentic Systems: Design and scale robust, secure, and production-ready multi-agent workflows, orchestrations, and advanced RAG architectures, in collaboration with Enterprise Architecture principles. Drive delivery by designing and building agentic solutions, spanning from piloting to full implementation Define Engineering Excellence: Establish ...

when not to Hands-on experience building, evaluating, and deploying machine learning models into production Practical experience with Large Language Models, including prompt engineering, RAG, fine-tuning, and agentic frameworks Practical experience with CI/CD, containerisation, and cloud-native deployment patterns Demonstrated experience delivering system design, application development, testing ...

across the organisation. Responsibilities Technical Leadership & Engineering Design Agentic Systems: Design and scale robust, secure, and production-ready multi-agent workflows, orchestrations, and advanced RAG architectures, in collaboration with Enterprise Architecture principles. Drive delivery by designing and building agentic solutions, spanning from piloting to full implementation Define Engineering Excellence: Establish ...

across the organisation. Responsibilities Technical Leadership & Engineering Design Agentic Systems: Design and scale robust, secure, and production-ready multi-agent workflows, orchestrations, and advanced RAG architectures, in collaboration with Enterprise Architecture principles. Drive delivery by designing and building agentic solutions, spanning from piloting to full implementation Define Engineering Excellence: Establish ...

high-quality production code while guiding technical direction Deep hands-on expertise building enterprise AI applications using large language models, AI agents, retrieval-augmented generation, prompt engineering, orchestration frameworks, evaluation methods, and model optimisation techniques Proven ability to take complex AI solutions from prototype … pipelines, and platform capabilities that support real-time and batch AI workloads at enterprise scale Implement advanced large language model applications using retrieval-augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integration Make key technical decisions while remaining accountable ...

high-quality production code while guiding technical direction Deep hands-on expertise building enterprise AI applications using large language models, AI agents, retrieval-augmented generation, prompt engineering, orchestration frameworks, evaluation methods, and model optimisation techniques Proven ability to take complex AI solutions from prototype … pipelines, and platform capabilities that support real-time and batch AI workloads at enterprise scale Implement advanced large language model applications using retrieval-augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integration Make key technical decisions while remaining accountable ...

custom orchestration frameworks . Integrate leading and self-hosted LLMs with tools, data and external systems using MCP and custom connectors . Implement RAG solutions using vector databases and hybrid retrieval where appropriate. Work across relational, document, key-value and graph databases depending on the problem. Develop prompt ...

OpenAI tooling or custom orchestration frameworks. Integrate leading and self-hosted LLMs with tools, data and external systems using MCP and custom connectors. Implement RAG solutions using vector databases and hybrid retrieval where appropriate. Work across relational, document, key-value and graph databases depending on the problem. Develop ...

when not to Hands-on experience building, evaluating, and deploying machine learning models into production Practical experience with Large Language Models, including prompt engineering, RAG, fine-tuning, agentic frameworks, skills. Demonstrated experience delivering system design, application development, testing, and operational stability for ML or data-intensive systems Strong communication skills ...