726 to 750 of 878 Retrieval-Augmented Generation Jobs in the UK

Kubernetes, and cloud-native infrastructure. Design and deploy scalable batch and real-time ML pipelines supporting customer-facing applications. Develop and operationalise LLM applications, RAG systems, and Generative AI capabilities for content understanding, summarisation, classification, and recommendation use cases. Build evaluation frameworks and observability solutions to monitor model quality, reliability … Intelligence, Statistics, Data Science, or a related STEM field. Experience applying Generative AI and LLMs to customer-facing products and designing, building, or scaling RAG systems. Experience in search, ranking, recommendations, personalisation, content intelligence, moderation, NLP, computer vision, or related AI domains. Experience with Airflow or comparable workflow orchestration platforms. ...

including API design, automated testing, asynchronous processing, dependency management and production debugging. Practical experience with agent orchestration, structured tool calling, workflow state, retrieval-augmented generation and human-in-the-loop patterns. Experience building secure integrations with enterprise APIs, repositories, cloud services, CI/ ...

systems (Kafka or similar) are a plus. Shipped AI in a real product, not only notebooks: LLM APIs, tool/function calling, retrieval-augmented generation, evaluation, or document/vision extraction. You can talk about failure modes (hallucination, cost, latency, prompt injection, PII). ...

looking for an experienced Agentic AI Engineer to join a pioneering team building next-generation AI solutions. You'll work on cutting-edge projects involving LLMs, Neo4j, Knowledge Graphs, GraphRAG, and AI Agents , helping unlock greater context, reasoning, and automation from their data. Key Responsibilities: Design and develop … teams to deliver production-ready AI systems Required Skills: Strong Python development experience Hands-on experience with Neo4j and Knowledge Graphs Experience with LLMs, RAG, or GraphRAG architectures Understanding of AI agents and autonomous workflows Cloud experience (AWS, Azure, or GCP) Desirable: LangGraph, LangChain, CrewAI, AutoGen, or similar frameworks Experience ...

Engineer to design, build, and deploy production-ready Generative AI systems. Role You will own the full lifecycle of GenAI applications, working with LLMs, RAG architectures, and agentic frameworks to deliver scalable, business-focused solutions. Responsibilities Build and deploy GenAI and RAG systems Develop agentic AI workflows Design prompts … clients and technical teams Requirements 1-3 years deploying LLM/GenAI systems in production Strong Python skills Experience with cloud platforms Knowledge of RAG, vector databases, and LLM frameworks Git and CI/CD experience Experience with LangChain and LangGraph AWS experience IT/Technology-related university degree (minimum ...

theoretically. We are already building it. We are looking for a Director, Generative AI Experience Engineering to help define and deliver the next generation of AI-native customer experiences across commerce, content and customer interfaces for some of the world’s most ambitious brands. This role sits … edge rendering - Component systems and frontend orchestration AI Ecosystems & Orchestration - Experience across modern AI ecosystems including OpenAI, Gemini, Claude and OSS models RAG pipelines, vector databases, embeddings and chunking Tool calling, agentic systems, orchestration and memory frameworks Prompt engineering, prompt management and context engineering AI observability, evaluations, governance and guardrails ...

Curve Maintain a current and deep understanding of the evolving Generative AI and Agentic AI landscape - frontier models, AI patterns, prompt engineering, fine-tuning, RAG architectures, and multi-agent orchestration. Translate complex technical concepts into clear business value propositions specific to Fund Services operations and clients. Fund Services is building … cloud architectures, distributed systems, and modern software development practices Deep understanding of the Generative AI ecosystem - frontier models, AI patterns, prompt engineering, fine-tuning, RAG, and agentic architectures Proven ability to move from problem identification to working prototype at pace - with a portfolio of real things built, not just planned ...

team and use case. Enablement & Integration Deploy vector database and retrieval infrastructure (e.g., Databricks AI Search) to support Document AI and RAG use cases. Align platform controls with Databricks and Unity Catalog governance patterns. Provide clear documentation, reusable patterns, and self-service onboarding guidance so delivery squads … from the crowd Experience with AI gateways, LLM proxies, or model-routing patterns. Exposure to AI observability tools, vector databases (Databricks AI Search), RAG pipelines, or non-deterministic system monitoring. Hands-on experience implementing policy-as-code, action logging, or AI kill switches. Direct experience with Okta, ZScaler, Entra ...

just getting started. Recognised by EGR as one of the most innovative startups in gaming, we\'re scaling fast and shaping the next generation of sports entertainment. We\'ve built a product that users genuinely love — with unlimited betting group chats, multi-game bet builders, and an experience … explain what broke and what you changed. Hands-on with an agent framework (LangGraph, LlamaIndex, Semantic Kernel, ADK or similar) and RAG in production: embedding models, vector stores, re-ranking, and knowing when a live query beats retrieval. Strong Python experience or similar, on a real engineering foundation: testing, version ...

Engine, SGP, Donovan, and others that power the most advanced LLMs and generative models in the world through world-class RLHF, human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI. … understanding of highly scalable and reliable distributed systems hosted on public cloud platforms. Experience building document retrieval and understanding systems, such as RAG, to accurately ingest large-scale client knowledge into client agents and applications. A strong track record of independently owning and delivering successful engineering projects, including ...

generating production-ready pipelines, infrastructure, and business logic. Build enterprise-grade data pipelines and AI workflows using reusable, governed patterns. Embed GenAI capabilities including RAG, vector search, and AI assistants into data platforms. Design and orchestrate AI agents and multi-agent workflows to enable intelligent automation. Apply MLOps and LLMOps … delivering data and AI platforms using Databricks. Strong knowledge of Databricks Lakehouse, Unity Catalog, MLflow, and AI workloads. Experience developing GenAI solutions such as RAG, embeddings, and LLM integration. Experience designing agentic workflows and orchestration patterns. Strong understanding of Azure AI services and their integration with Databricks. Experience using ...

site role. In this role, you will have the opportunity to: Design and deliver end-to-end LLM-powered applications, including RAG systems and agent workflows. Transform prototypes into reliable, scalable production services with deployment, monitoring, and failure handling. Integrate and operate external model APIs, managing constraints such as latency … requirements of the job include: Strong software engineering capability, primarily in TypeScript and Python with backend and API development skills. Expertise in GenAI systems: RAG, prompt engineering, LLM integration, and agentic workflows. Cloud and infrastructure knowledge, including deployment, containerization, and CI/CD practices. Understanding of machine learning fundamentals, including ...

Strands Agents Build, integrate, and maintain MCP (Model Context Protocol) servers that expose capabilities to AI agents across client platforms Architect and implement RAG pipelines using Amazon Bedrock Knowledge Bases, managing vector stores, embeddings, and document ingestion from S3 and other sources Apply the A2A (Agent-to-Agent) protocol … integrated MCP servers in a production or near-production context Strands Agents — familiarity with the framework and its application to agentic workflows on AWS RAG implementation — knowledge base design, chunking strategy, vector store configuration, and retrieval evaluation Python — strong, applied proficiency in an AWS and AI context ...

release increment, including data flows, integration sequencing, and non-functional requirements (performance, scalability, security) baked in at design time Own the technical implementation pattern (RAG pipeline design, prompt/agent orchestration, model integration via APIs) and work directly with data science/ML engineering teams during build. Sit in sprint … program/product roadmap into solution designs and detailed architecture specs for Commercial Digital Solutions Define architecture patterns for GenAI/LLM-based solutions (RAG, agentic workflows, model orchestration), evaluate build-vs-buy for AI capabilities Architect integration patterns between Commercial systems and the enterprise data platform/data lake ...

production rollout Troubleshoot complex technical issues and optimise AI application performance in enterprise production environments Design scalable enterprise AI architectures using LLMs, Prompt Engineering, RAG, AI Agents, MCP, fine-tuning, inference optimisation, vector databases, API integration, workflow orchestration, and cloud-native AI services Collaborate with Product, Engineering, and Research teams … LLMs), machine learning, AI platforms, cloud computing, software engineering, MLOps, solution architecture, or customer-facing technical consulting Hands-on experience with LLMs, Prompt Engineering, RAG, AI Agents, MCP, model fine-tuning, inference optimisation, vector databases, AI orchestration frameworks, API integration, and cloud-native AI architectures Experience developing or deploying enterprise ...

complex, multi-source datasets from local authorities. You will engineer high-performance training pipelines, build embedding-based and sequence models, implement LLM and RAG workflows, and develop containerised model services that integrate directly into the OneView platform. This includes hands-on work with model architectures, feature engineering, model optimisation, performance … with client needs. Troubleshoot and resolve complex modelling and pipeline issues throughout delivery. AI engineering Build and integrate LLM based components including embedding pipelines, RAG workflows and text analysis models. Develop and deploy agentic and multicomponent AI systems using modern ML frameworks. Engineer high performance NLP and sequence models ...

dependency mapping, service impact, and fault propagation.* Work with AI/LLM engineers to provide high-quality features, embeddings, metadata, and contextual datasets for RAG and agentic AI systems.* Define network data quality rules, correlation logic, and entity resolution methods.* Create reusable analytical models for RAN, Core, IP/MPLS … assessment.* Experience with graph analytics, entity relationship mapping, or knowledge graph concepts.* Understanding of LLMs and how structured data can support AI agents and RAG systems.* Experience with BigQuery or similar data warehouse/analytics platforms.**Required Technical Skills*** Python.* Statistics and ML basics.* Network KPI modelling.* RAN, Core ...

productionisation of our machine learning work end to end: real-time serving against SLAs, a standardised CI/CD path to production, GenAI and RAG system deployment, and the monitoring infrastructure that catches drift and quality degradation before business KPIs move. You'll work on a GCP-native stack — Vertex … company language and is used for business-wide communication. It will also help you to have... Experience with LLM evaluation, observability tooling, or RAG systems in production Experience defining MLOps standards across multiple teams Exposure to cost and latency management for GenAI systems Benefits This is a fully remote position ...

audits within Tech teams to assess quick‐win candidates, needs for architect‐level guidance, and non‐worth‐pursuing cases. Create technical training content on RAG, AI agent design, prompt engineering, and API integration for a Tech audience. Provide floor support during workshops, including live debugging and troubleshooting. Governance, Security & Process … DevOps) with a subsequent move into enablement, consultancy, or internal transformation. Proven experience coaching technical teams to build and deploy AI agents or RAG pipelines in production. Hands‐on with at least one low‐code/no‐code automation platform (e.g., n8n). Strong prompt engineering knowledge and ability ...

work includes:* Building and operating LSEG AI Assist, an in‐house agentic experience capable of reasoning, planning, and tool‐calling.* Operating QAS, the enterprise RAG and search layer used to ground agent responses in approved data sources.* Delivering a production Internal MCP Gateway providing discovery, security, policy enforcement, observability … understanding of Model Context Protocol (MCP), including server and tool patterns.* FastAPI and REST API design and implementation experience.* Experience with prompt engineering and RAG‐based architectures.* Containerisation and Kubernetes‐based deployment experience.* Ability to work across platform, product, and governance boundaries in an enterprise environment. Career Stage: ManagerLondon Stock ...

mainly Python across the modern AI engineering stack - harness engineering, skills and tool building, agent workflows and orchestration, agent hosting and sandboxing, guardrails, evals, RAG and context engineering, and tokenomics (cost, latency, model selection). Production-grade LLM system experience is the core requirement. You will work on three types … your primary language. LLM-powered systems in production: real-world experience working with models via APIs - harnesses, orchestration, structured output, tool use and agents, RAG where appropriate. Evals and quality: designing evaluation sets, measuring accuracy, recall and precision for LLM steps, regression-testing prompts and workflows. Safety and guardrails ...

mainly Python across the modern AI engineering stack - harness engineering, skills and tool building, agent workflows and orchestration, agent hosting and sandboxing, guardrails, evals, RAG and context engineering, and tokenomics (cost, latency, model selection). Production-grade LLM system experience is the core requirement. You will work on three types … your primary language. LLM-powered systems in production: real-world experience working with models via APIs - harnesses, orchestration, structured output, tool use and agents, RAG where appropriate. Evals and quality: designing evaluation sets, measuring accuracy, recall and precision for LLM steps, regression-testing prompts and workflows. Safety and guardrails ...

plane for staff and customer authentication. Contribute to AI-enabled quotation and knowledge tools, including human-in-the-loop quote drafting and retrieval-augmented knowledge features. Collaborate closely with mobile, product and engineering colleagues where your APIs are consumed across different surfaces. Take part in architecture … through to delivery. Useful Additional Experience Exposure to React or React Native, particularly where APIs support web or mobile products. Experience with retrieval-augmented generation, vector search or integrating LLM-based features into production products. Experience working in a scale-up, transformation programme ...

plane for staff and customer authentication. Contribute to AI-enabled quotation and knowledge tools, including human-in-the-loop quote drafting and retrieval-augmented knowledge features. Collaborate closely with mobile, product and engineering colleagues where your APIs are consumed across different surfaces. Take part in architecture … through to delivery. Useful Additional Experience Exposure to React or React Native, particularly where APIs support web or mobile products. Experience with retrieval-augmented generation, vector search or integrating LLM-based features into production products. Experience working in a scale-up, transformation programme ...

plane for staff and customer authentication. Contribute to AI-enabled quotation and knowledge tools, including human-in-the-loop quote drafting and retrieval-augmented knowledge features. Collaborate closely with mobile, product and engineering colleagues where your APIs are consumed across different surfaces. Take part in architecture … through to delivery. Useful Additional Experience Exposure to React or React Native, particularly where APIs support web or mobile products. Experience with retrieval-augmented generation, vector search or integrating LLM-based features into production products. Experience working in a scale-up, transformation programme ...