101 to 125 of 419 Retrieval-Augmented Generation Jobs in the UK

agentic architectures. Good understanding of modern AI concepts and practices, including: Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) Prompt Engineering AI Guardrails AI Evaluation Frameworks Experience with AI frameworks such as Agent Development Kit (ADK), LangChain, LangGraph, CrewAI, or similar. Experience working with ...

extracts. Prototype AI-assisted internal tools including applications leveraging LLM APIs, embedding-based search, and retrieval-augmented generation (RAG) to demonstrate near-term business value from Third Bridge s content assets. Collaborate closely with the analytics peers to align on data definitions, shared datasets ...

will combine deep software engineering experience, excellent judgment and a demonstrable interest in AI to help shape the way we build our next generation of products. Initially working in a small, focused team, you will help lead and progress the development of our foundational AI-native/… principles (REST, SPA...). Microservice architecture, AWS and DevOps. Building LLM-powered features: tool-calling, retrieval-augmented generation (RAG), evaluation/eval harnesses and observability (desirable). Familiarity with model-agnostic tooling and routing, e.g. a proxy/gateway layer (desirable). Agile manifesto ...

enterprise environment. Driving build versus buy evaluations and recommending appropriate technologies. Delivering solutions including retrieval-augmented generation (RAG), agentic AI, automation, and intelligent decision-support tools. Working closely with engineering, security, governance, and business stakeholders. Navigating complex and ambiguous problem spaces to identify practical … fundamentals and experience building scalable applications. Experience making architecture and technology decisions within enterprise organisations. Strong knowledge of modern AI engineering practices, including LLMs, RAG, agentic workflows, and MLOps concepts. Experience working with cloud platforms, ideally AWS. Ability to balance innovation with risk, governance, and operational requirements. Strong stakeholder management ...

solution approaches across the modern AI stack, including Large Language Models (LLMs), Prompt Engineering, Retrieval-Augmented Generation (RAG), Vector Databases and MLOps. Support clients in navigating cloud, security, data governance and operating model considerations required for enterprise AI adoption. Communicate complex machine learning ...

products and wider AI strategy.You'll design, build and operate the cloud-native platform supporting Retrieval-Augmented Generation (RAG), LLM orchestration, vector search and production AI services, working closely with AI Engineering, DevOps and Security teams to deliver scalable, secure and reliable AI solutions. … Role scope: Designing, building and operating AI platform infrastructure across Microsoft Azure. Developing and optimising RAG pipelines, AI services and platform components. Deploying infrastructure using Terraform and supporting containerised applications. Building and maintaining CI/CD pipelines using GitHub and Azure DevOps. Improving observability, monitoring and platform resilience. Supporting vector ...

infrastructure development practices. Advanced LLM Orchestration : Experience designing LLM applications beyond basic APIs, including retrieval-augmented generation (RAG) pipelines, orchestration logic, and tool/agent workflows. API Integration : Strong awareness of REST APIs and webhooks to effectively connect AI agents with backend systems … Development Kit (ADK), LangChain, or similar multi-agent orchestration frameworks. Practical familiarity with vector databases, hybrid search techniques, and advanced chunking strategies for optimizing RAG systems. Knowledge of or previous experience within the travel sector or fast-paced B2C e-commerce environments. Familiarity with version control tools (Git) and working ...

modern AI technologies and concepts, including: Generative AI Large Language Models (LLMs) Prompt Engineering Retrieval-Augmented Generation (RAG) Vector Databases AI Solution Architecture MLOps Principles AI Governance and Risk Considerations Candidates are not expected to be hands-on developers but should be capable ...

technology teams to optimise semantic models for AI readiness. Apply data science, machine learning, NLP, and advanced analytics techniques to enhance insight generation and anomaly detection. Contribute to the organisation's long-term Data & AI strategy, influencing senior stakeholders and executive leadership. Establish best practices, standards, and governance … shape direction and define solutions. Desirable Experience Microsoft Copilot, Azure OpenAI, or similar AI platforms. Retrieval-Augmented Generation (RAG) and semantic grounding techniques. Executive-level analytics solutions and trusted AI implementations. Highly regulated or data-governed environments. What We Offer Opportunity to influence enterprise ...

hackajob is collaborating with Google to connect them with exceptional professionals for this role. Google Cloud's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. We're looking for engineers who bring fresh ideas … where AI is profoundly data-centric, the quality data used for training, fine-tuning, or Retrieval-Augmented Generation (RAG) has greater impact one end product performance than almost anything else. Your mission is to improve the time to model quality for users, achieved ...

responsible for: Designing and developing AI agents using modern LLM frameworks Building Retrieval-Augmented Generation (RAG) solutions using enterprise knowledge sources Developing and deploying Machine Learning models for intelligent decision-making Integrating AI services with enterprise APIs and data platforms Engineering prompts, tool calling ...

understanding of the modern AI ecosystem, including: Generative AI Large Language Models (LLMs) Prompt Engineering Retrieval-Augmented Generation (RAG) Vector Databases MLOps Experience leading the implementation of enterprise-scale AI solutions and being accountable for delivery outcomes. Knowledge of cloud platforms such ...

understanding of the modern AI ecosystem, including: Generative AI Large Language Models (LLMs) Prompt Engineering Retrieval-Augmented Generation (RAG) Vector Databases MLOps Experience leading the implementation of enterprise-scale AI solutions and being accountable for delivery outcomes. Knowledge of cloud platforms such ...

implementing AI agents, automation workflows and AI-powered business solutions. Strong understanding of prompt engineering, Retrieval-Augmented Generation (RAG), tool/function calling and LLM integrations. Excellent business analysis, stakeholder management and workshop facilitation skills. Ability to translate complex business requirements into practical, scalable ...

Apply advanced techniques including optimisation, forecasting, statistical modelling and AI Develop LLM-based solutions, including: Retrieval-Augmented Generation (RAG) Fine-tuning and prompt engineering Agent-based workflows Work with large and complex datasets, both structured and unstructured Write clean, scalable Python code for model … Pandas, NumPy, SciPy or similar) Solid grounding in statistics, probability and data analysis AI & LLM Experience Hands-on experience with large language models, including: RAG pipelines Fine-tuning approaches Agent-based architectures A practical understanding of how to apply LLMs to real-world use cases Tools & Engineering Experience with model ...

Guide architecture across teams, helping reduce complexity and establish common patterns. Partner with AI and Retrieval teams to build scalable search and RAG capabilities. Lead complex cross-team technical initiatives from design through production. Improve platform reliability, scalability, observability, and developer experience. Contribute hands-on to critical technical … with cloud infrastructure (AWS), Kubernetes, APIs, and modern backend development. Familiarity with AI-powered systems, Retrieval-Augmented Generation (RAG), search platforms, or vector databases. Strong programming experience in Python or another modern backend language. Comfortable influencing technical direction without direct authority. Excellent communication skills ...

objectives. Nice to have Experience with Microsoft Fabric, Power Platform or Microsoft 365. Exposure to Retrieval-Augmented Generation (RAG), AI agents or AI automation. Microsoft certifications in Azure or AI technologies. Experience working within large enterprise organisations. Contract details 12-month contract ...

agentic workflow frameworks Evaluation harnesses, evaluation datasets and automated test suites for AI outputs Retrieval-augmented generation (RAG), vector search, embeddings and retrieval pipelines (desirable) LLM orchestration frameworks (LangChain, Semantic Kernel or equivalents – desirable) Technical and Professional Expertise Strong software engineering background ...

services Contribute to the development and evolution of a production-grade AI platform Build and integrate AI-powered features such as LLM-driven services, RAG pipelines, and agent-based workflows into core systems Leverage tools like GitHub Copilot, Claude Code, Cursor, or similar AI-assisted engineering workflows. Apply modern software … 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 ...

resolve failures in real-time. Experience connecting agents to enterprise knowledge bases and optimizing Retrieval-augmented generation (RAG) chunking to prevent hallucinations. Ability to travel up to 50% of the time. Track record of troubleshooting live, high-traffic systems during critical windows. About ...

Integrating AI capabilities into applications and workflows Developing and evaluating LLM-based solutions Building Retrieval-Augmented Generation (RAG) systems Testing, debugging, and improving non-deterministic AI systems Evaluating AI performance, limitations, and risks Applying machine learning concepts to solve complex problems What theyre looking … Experience with Python and AI/ML technologies Understanding of LLMs, machine learning concepts, and statistical methods Experience with RAG, model evaluation, or AI applications Strong analytical thinking and problem-solving ability Interest in responsible and secure AI development Experience in defence, national security, AI safety, or constrained environments would ...

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 ...

across the others. Agentic AI & LLM Engineering Design and build production‐grade agentic systems using major LLM SDKs and agent frameworks; deep knowledge of RAG, MCP servers and prompt engineering at scale. Strong opinions on secure, resilient enterprise deployment of LLM‐powered systems; current knowledge of the latest model capabilities ...

based solutions such as copilots, chatbots, and knowledge assistants Understanding of prompt engineering, grounding, and retrieval-augmented generation (RAG) Ability to apply governance, security, and ethical considerations in AI deployments Strong background across the Microsoft data and automation stack, including Power BI, Power Automate ...