151 to 172 of 172 Retrieval-Augmented Generation Jobs in the UK

based workflows, integrating AI platforms, and delivering scalable cloud-native solutions. Responsibilities AI Agent Engineering Design and implement AI agents, including: Retrieval (RAG) Orchestration workflows Tool/function invocation Policy-based routing Build evaluation frameworks for accuracy, latency, and reliability Implement observability and monitoring for agent lifecycle … engineering experience Strong experience with cloud-native systems (APIs, microservices, containers, serverless) Experience building and deploying AI/LLM-based systems in production (agents, RAG, orchestration) Proficiency in Python, Java, or similar backend languages Experience with: CI/CD pipelines Infrastructure as code Monitoring and observability tools Hands-on experience ...

Senior AI/ML Engineer (LLM's/RAG Pipeline Development)Guildford, Surrey (Fully Onsite)£85000 - £125,000 + 25% Bonus, 10% Pension, Private Medical.- Must have a Masters or PhD Degree in a STEM/Computing discipline.- Must work full-time, 5 days a week in the Guildford office. ...

deploying production AI applications using LLMs Designing agentic workflows and multi-step AI pipelines Developing scalable backend APIs and cloud-native services Creating RAG pipelines, embeddings and AI retrieval systems Improving AI reliability through testing, evaluation and guardrails Working closely with engineering, product and business teams Contributing … architecture decisions and AI best practice Helping shape the future direction of AI capability across the business Tech environment Python, AWS, APIs, LLMs, RAG, Docker, Kubernetes, microservices, cloud-native architecture and modern AI engineering tooling. What they're looking for Strong Python backend engineering experience Experience building and deploying ...

ecosystems and scalable LLM architectures. Key Requirements We are particularly interested in engineers with proven, production-grade experience across: Azure AI Foundry (agentic workflows, RAG systems, orchestration, deployment pipelines) Microsoft Copilot Studio (enterprise copilots, agents, integrations with M365/Power Platform) End-to-end LLM application delivery in production environments … integration, secure deployment patterns, and enterprise data connectivity Experience building and scaling RAG-based architectures and agentic systems Strong understanding of MLOps/LLMOps principles in cloud-native environments Overview of Role Design and deploy enterprise-grade AI agents and copilots embedded into business workflows Build scalable LLM solutions leveraging ...

depending on experience. Experience and skills required for the AI Platform Engineer (MLOps/DevOps) MLOps or ML platform engineering RAG architectures, embeddings, or vector search Model serving, observability or performance optimisation Data/AI workflow orchestration in Databricks or similar ecosystems Cloud infrastructure engineering (AWS-focused environments) Kubernetes, containerisation … platform reliability Running production workloads with high availability requirements Role and Responsibilities for the AI Platform Engineer (MLOps/DevOps) Design and optimise scalable RAG pipelines and vector search systems Orchestrate multi-model AI services with a focus on latency, resilience and performance Productionise GenAI workflows and ensure they operate ...

highly regulated financial environment? This is a genuinely exciting opportunity to join a top banking organisation investing heavily in automation, AI and next-generation engineering capabilities. You'll be building full-stack applications, AI-powered tools, automations and workflow solutions that directly improve business operations and user productivity … enjoys combining modern software engineering with emerging AI technologies. Tech & Projects Include: Python (FastAPI/Flask) JavaScript, REST APIs & SQL Server AI & LLM applications RAG pipelines & agentic AI workflows Locally hosted open-source LLMs Microsoft Power Platform LangChain, LangGraph, Semantic Kernel & Copilot Studio exposure Why this role stands out: Real ...

guide AI development Key Responsibilities Lead the development and deployment of AI-powered tools to improve efficiency and productivity Design advanced solutions including automation, RAG, and LLM-based workflows Collaborate with stakeholders and internal teams to identify and deliver strategic AI initiatives that deliver measurable business value and increase productivity … experience in AI engineering or software engineering Strong Python experience Proven experience of building and deploying AI solutions end-to-end Experience with RAG and LLM-based solutions, and with workflow design (e.g. N8N, Power Automate, LangChain, etc.) Solid understanding of AI architectures and data pipelines Knowledge of security, compliance ...

enterprise Agentic AI platforms. You will move beyond traditional software QA to build continuous safety pipelines, ensuring our non-deterministic LLM agents, RAG systems, and tool integrations are secure, resilient, and compliant before production release. Key Responsibilities Automated Adversarial Testing: Build and integrate automated red teaming suites into CI/… within FCA, DORA, or EU AI Act frameworks. AWS Bedrock Ecosystem: Hands-on experience configuring, testing, and bypassing Bedrock Guardrails, Agents, and Knowledge Bases (RAG) . AI Security & Fundamentals: Solid understanding of Foundation Models, tool use (function calling), OWASP LLM Top 10 , and NIST AI RMF . Automation Stack: Strong ...

enterprise Agentic AI platforms. You will move beyond traditional software QA to build continuous safety pipelines, ensuring our non-deterministic LLM agents, RAG systems, and tool integrations are secure, resilient, and compliant before production release. Key Responsibilities Automated Adversarial Testing: Build and integrate automated red teaming suites into CI/… within FCA, DORA, or EU AI Act frameworks. AWS Bedrock Ecosystem: Hands-on experience configuring, testing, and bypassing Bedrock Guardrails, Agents, and Knowledge Bases (RAG) . AI Security & Fundamentals: Solid understanding of Foundation Models, tool use (function calling), OWASP LLM Top 10 , and NIST AI RMF . Automation Stack: Strong ...

Development: LLM fine-tuning adapting pretrained LLMs for specific tasks using techniques like parameterefficient fine-tuning (PEFT) (e.g., LoRA, QLoRA). Implementing Retrieval-Augmented Generation pipelines to enhance the knowledge and accuracy of LLMs. Utilizing vector databases for efficient storage and retrieval ...

Skills, and secure API integrations that expose enterprise systems safely to LLM-powered agents. You'll also contribute to agent planning, tool-calling frameworks, RAG architecture, and the broader developer experience for internal AI platform adoption. Key requirements: * Strong Python engineering experience * Experience with LLMs, AI agents, and agentic reasoning … patterns * FastAPI and REST API development * RAG and prompt engineering experience * Kubernetes/containerisation knowledge * Exposure to MCP or similar AI tooling frameworks * Ability to work across platform and product engineering teams This is an opportunity to help shape the future of enterprise AI infrastructure within a large-scale, modern ...

workflow tooling for industrial and engineering environments Developing APIs, integrations, and backend systems connecting operational data into AI-driven products Working across AI agents, RAG pipelines, embeddings, vector databases, and LLM orchestration Designing scalable cloud-native infrastructure within AWS Building customer-facing product functionality across frontend and backend systems Helping … Tech environment: Python (~60% of codebase) TypeScript/JavaScript/Node.js/FastAPI AWS REST APIs Vector databases AI agents/LLM integrations/RAG CI/CD and cloud infrastructure They're looking for someone who: Enjoys startup environments and ownership-heavy roles Can operate across frontend + backend ...

machine learning or software engineering environments. You will work on a broad range of AI challenges spanning multimodal AI, machine learning, computer vision, LLMs, RAG architectures, transformer models, audio processing and intelligent automation features within a highly collaborative engineering environment. Location: Cambridge, UK. Hybrid working model - minimum of 4 days … Kotlin, TypeScript and React, prior experience with these technologies is not required Interest and exposure across AI technologies such as machine learning, LLMs, RAG systems, computer vision, multimodal AI or transformer architectures Ability to work across both research/prototyping and production engineering Strong problem-solving ability and analytical thinking ...

passionate about shaping how organisations deploy and scale AI? I'm supporting a leading business in the Insurance Sector as they build next-generation AI and integration capabilities across their enterprise. You'll work at the intersection of GenAI engineering, cloud architecture, and enterprise integration, helping define … What you'll be doing Designing and deploying AI-enabled solutions across cloud environments. Leading on GenAI orchestration (LangChain, Autogen, Google ADK). Architecting RAG pipelines, vector database integrations, and prompt management strategies. Building guardrail frameworks to prevent hallucinations, injection and bias. Designing API-first and event-driven architectures. Influencing ...

Financial Services team. You'll be at the intersection of Agentic AI and Zero Trust , building the secure backbone for the next generation of AWS Bedrock deployments. The Baseline FinServ Pro: You know the UK landscape inside out- DORA , FCA Operational Resilience , and the EU AI Act aren … Hands-on experience with AWS Bedrock (Agents, Knowledge Bases, Guardrails, and the full model lifecycle). AI/ML Foundations: Deep understanding of FMs, RAG , non-deterministic agents, and complex tool-use. Secure AI: Expert-level knowledge of OWASP LLM Top 10 and Agentic AI threats . Familiarity with NIST ...

passionate about shaping how organisations deploy and scale AI? I'm supporting a leading business in the Insurance Sector as they build next-generation AI and integration capabilities across their enterprise. Have you got what it takes to succeed The following information should be read carefully … What you'll be doing Designing and deploying AI-enabled solutions across cloud environments. Leading on GenAI orchestration (LangChain, Autogen, Google ADK). Architecting RAG pipelines, vector database integrations, and prompt management strategies. Building guardrail frameworks to prevent hallucinations, injection and bias. Designing API-first and event-driven architectures. Influencing ...

company. Requirements Key Responsibilities 1. Data platform evolution Take our current AWS/Snowflake/S3/Postgres setup and enable AI/RAG, product consumption, and multi-tenant access. Work with the Technical Architect to keep this aligned with our event-driven and security model. 2. Data acquisition & ingestion … observable, repeatable, and documented so research and analytics can plug in new sources without rework. 3. AI as a consumption layer Build LLM/RAG endpoints on top of our data and content as a service. Expose these services to Data Insights to enable users to ask, explore, and generate ...

client teams to design, build and deploy AI driven solutions on the platform Translate complex research and business questions into production ready LLM and RAG workflows Debug, optimise and scale LLM applications in real world, client facing environments Build and integrate data pipelines and services across cloud platforms and modern … Your Skills and Experience Strong commercial engineering experience in AI, machine learning or data focused roles Hands on experience with retrieval augmented generation and production LLM workflows Confidence debugging and improving end to end LLM systems in live environments Experience working with modern data ...

Bristol (Hybrid) | Permanent An advanced technology and research organisation is seeking a Principal Research Engineer/Research Fellow to help shape next-generation distributed data-sharing and industrial AI solutions across sectors including energy, infrastructure and industrial systems. This role sits within a highly specialised R&D environment … sharing platforms Industrial AI/edge AI Cloud & microservices architecture Python, Java & SQL PostgreSQL, MongoDB, Spark or similar tooling Knowledge graphs/semantic technologies RAG, agentic AI or generative AI concepts Candidates from industrial R&D, advanced engineering or applied AI environments would be particularly relevant. A PhD is preferred ...

Agents, and Denial-of-Wallet-into rigorous test cases. AWS Bedrock Mastery: Overseeing the secure model lifecycle, including the configuration of Guardrails, Knowledge Bases (RAG), and Agentic tool-use. Regulatory Stewardship: Mapping the firm's safeguard catalogue against the specific demands of DORA, FCA Operational Resilience … appetite. Essential Skill Proven track record in UK Financial Services with deep knowledge of FCA compliance and DORA. Solid grasp of Foundation Models (FMs), RAG, and the risks of non-deterministic agentic behavior. Mastery of OWASP LLM Top 10 and the NIST AI Risk Management Framework. Hands-on experience securing ...

Agents, and Denial-of-Wallet-into rigorous test cases. AWS Bedrock Mastery: Overseeing the secure model lifecycle, including the configuration of Guardrails, Knowledge Bases (RAG), and Agentic tool-use. Regulatory Stewardship: Mapping the firm's safeguard catalogue against the specific demands of DORA, FCA Operational Resilience … appetite. Essential Skill Proven track record in UK Financial Services with deep knowledge of FCA compliance and DORA. Solid grasp of Foundation Models (FMs), RAG, and the risks of non-deterministic agentic behavior. Mastery of OWASP LLM Top 10 and the NIST AI Risk Management Framework. Hands-on experience securing ...

users ranging from highly technical teams to more non technical stakeholders Designing and building AI driven workflows, including LLM and retrieval augmented generation use cases Integrating knowledge graph data into customer environments using APIs, graph databases or analytical platforms Partnering with a solutions consultant … commercial experience building and deploying AI or LLM powered systems into production Hands on experience designing, building and debugging LLM workflows, including familiarity with RAG approaches Experience working with modern data platforms and warehouses such as Snowflake or BigQuery Exposure to graph databases and knowledge graphs is highly desirable, (Neo4J ...