226 to 250 of 842 Retrieval-Augmented Generation Jobs in the UK

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 ...

associated data science ecosystem (Pandas, NumPy, Scikit-learn).* Machine learning techniques including classification, regression, clustering and anomaly detection.* Experience with Generative AI, LLMs, RAG architectures or agent-based AI solutions.* SQL and large-scale data analysis.* Cloud-based data science platforms and MLOps practices.* Data visualisation and storytelling tools.### ...

implementing end-to-end AI/ML solutions from development through to deployment and monitoring * Building scalable Generative AI and Agentic AI solutions, including RAG pipelines and orchestration frameworks * Implementing MLOps frameworks, governance and best practices * Translating business requirements into cloud-native AI and data architectures across Azure ...

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 ...

including data structures, algorithms, and software design patterns.*** **Agentic AI: Hands-on experience with AI Agents, Agent Development Kit (ADK), multi-agent frameworks, and RAG integration.*** **MLOps/DevOps: Experience with Kubeflow, Docker, Kubernetes, and Linux environments.*** **CI/CD: Proficiency in building and managing continuous integration and deployment pipelines. ...

development, with the judgement to know when to lean on them and when not to Practical experience with Large Language Models, including prompt engineering, RAG, and/or agentic frameworks Hands-on experience taking solutions from prototype to production, including CI/CD, containerisation, and cloud-native deployment Proven ability ...

with Python and modern AI/ML frameworks such as PyTorch and/or TensorFlow . Deep understanding of LLMs, Generative AI, prompt engineering, RAG architectures and vector databases . Experience with frameworks and tooling such as LangChain, LangGraph or similar open-source AI toolkits . Experience fine-tuning, evaluating ...

including data structures, algorithms, and software design patterns. Agentic AI: Hands-on experience with AI Agents, Agent Development Kit (ADK), multi-agent frameworks, and RAG integration. MLOps/DevOps: Experience with Kubeflow, Docker, Kubernetes, and Linux environments. CI/CD: Proficiency in building and managing continuous integration and deployment pipelines. ...

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 strict coding standards, code ...

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 ...

them and when not toHands-on experience building, evaluating, and deploying machine learning models into productionPractical 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 systemsStrong communication skills with ...

helping shape its long-term AI strategy Designing and building AI applications, assistants and workflow automation tools Creating Retrieval Augmented Generation (RAG) solutions using internal business data Working with stakeholders to identify opportunities where AI can reduce complexity and improve efficiency Designing scalable … operations, warranty and aftersales Current technologies include: Python SQL OpenAI & Anthropic APIs N8N Copilot Studio PostgreSQL AWS Docker & Kubernetes Experience with AI agents, LLMs, RAG or agentic AI frameworks would be highly beneficial. About you We're open to different backgrounds. You might already be working as an AI Engineer ...

organisations from identifying opportunities through to enterprise adoption and measurable business impact. Strong understanding of Generative AI, Agentic AI, Retrieval Augmented Generation (RAG), intelligent assistants and AI-enabled automation, with awareness of major enterprise AI ecosystems such as OpenAI, Google, Anthropic and Microsoft. Experience … Strategy Director, Enterprise AI Consultant, AI Consulting Lead, Data & AI Strategy Consultant, Generative AI, GenAI, Agentic AI, AI Agents, RAG, Retrieval Augmented Generation, Decision Intelligence, Intelligent Automation, AI Operating Models, AI Architecture, Data Platforms, Semantic Layers, Data Products, Responsible AI, AI Governance, Cloud Data ...

organisations from identifying opportunities through to enterprise adoption and measurable business impact. Strong understanding of Generative AI, Agentic AI, Retrieval Augmented Generation (RAG), intelligent assistants and AI-enabled automation, with awareness of major enterprise AI ecosystems such as OpenAI, Google, Anthropic and Microsoft. Experience … Strategy Director, Enterprise AI Consultant, AI Consulting Lead, Data & AI Strategy Consultant, Generative AI, GenAI, Agentic AI, AI Agents, RAG, Retrieval Augmented Generation, Decision Intelligence, Intelligent Automation, AI Operating Models, AI Architecture, Data Platforms, Semantic Layers, Data Products, Responsible AI, AI Governance, Cloud Data ...

earlier in the lifecycle. You will also support the adoption of AI/GenAI capabilities in practical QE use cases such as test generation, defect analysis, and quality reporting. What you'll be doing: Design, build, test, and maintain quality engineering solutions across the SDLCContribute to the development … ideally in testing, automation, or developer productivity use cases. This may include experience with: AI application patterns Retrieval-Augmentation Generation (RAG)prompt engineeringtool callingagentic workflowsevaluation of LLM-based outputs Frameworks and orchestration LangChainLangGraphCrewAI Models and platforms OpenAI and/or Azure OpenAI Vector search and retrieval ...

Cypher query language (e.g., Neo4j) or a strong interest in graph data modeling Familiarity with vector search, embeddings, or retrieval‐augmented generation (RAG) patterns Exposure to large language model serving, agentic patterns (tool/function calling, Model Context Protocol), or platforms such as Bedrock ...

software-engineering skills. Experience developing, integrating and operating enterprise software or AI applications. Practical understanding of LLMs, agentic AI, tool calling and retrieval-augmented generation. Experience building APIs, services and data pipelines, with Docker and cloud or on-premises deployments. Understanding of authentication, networking, access control ...

doesn't make sense, and delivering in short, time-boxed cycles where validated outcomes matter more than perfect solutions. Agentic AI/LLMs: Building RAG pipelines, tool-augmented agents and agentic workflows; familiar with prompt engineering, context management, evaluation and observability. Agent fundamentals: Understands how agents use memory ...

modern AI stack including Vector DBs, orchestration and durable execution frameworks, and LLM APIs. Experience building APIs, services and data retrieval pipelines (RAG, vector search etc) to feed data into LLMs. Deep proficiency with Python for both data and ML engineering, SQL and major cloud providers. An extensive ...

Experience working on recommendation systems, LLM applications or other AI/ML systems Practical experience with Kubernetes, EKS, Docker, MLOps Prior exposure to LLMs, RAG, Knowledge Graph Technologies, OpenSearch and vector databases Prior experience collaborating with data scientists J.P. Morgan is a global leader in financial services, providing strategic advice ...

/inference optimization, MLOps lifecycle management, and integration with cloud‐native services. GenAI Leadership: Serve as a practitioner for enterprise GenAI solutions, specializing in RAG architectures, agentic systems (including tool‐calling, multi‐agent orchestration, and guardrails), AI observability, and natural language querying of structured data. Provide advanced technical support ...

/inference optimization, MLOps lifecycle management, and integration with cloud-native services. GenAI Leadership: Serve as a practitioner for enterprise GenAI solutions, specializing in RAG architectures, agentic systems (including tool-calling, multi-agent orchestration, and guardrails), AI observability, and natural language querying of structured data. Provide advanced technical support ...

Experience working on recommendation systems, LLM applications or other AI/ML systems Practical experience with Kubernetes, EKS, Docker, MLOps Prior exposure to LLMs, RAG, Knowledge Graph Technologies, OpenSearch and vector databases Prior experience collaborating with data scientists J.P. Morgan is a global leader in financial services, providing strategic advice ...

experience with cloud platforms (AWS, Azure, GCP) Experience with ML frameworks (PyTorch, TensorFlow, scikit-learn) and Gen AI/Agentic frameworks (LangGraph, LangChain, VectorDBs, RAG) Understanding of responsible AI practices, including guardrails, hallucination mitigation, and output quality management for AI systems Experience designing and scaling low-latency, customer-facing ...

productivity, service improvement or risk reduction. The role will design and deliver pragmatic AI-enabled workflow improvements, including generative AI, agentic workflows, retrieval-augmented generation, process automation, analytics and decision-support solutions where appropriate. The role will act as the engineering bridge between … policy and control constraints. Preferred: Enterprise GenAI – Experience implementing generative AI or agentic AI solutions in enterprise environments. Knowledge workflows – Experience with retrieval-augmented generation, vector search, knowledge management, document intelligence or workflow orchestration. Regulated industry – Experience in banking, capital markets, financial services ...