251 to 275 of 842 Retrieval-Augmented Generation Jobs in the UK

production-ready solutions. Key Responsibilities Design and develop AI-powered applications using GenAI , LLMs , NLP , and Agentic AI technologies. Build intelligent RAG (Retrieval-Augmented Generation) solutions leveraging Embeddings and Vector Databases . Develop and orchestrate AI Agents and Multi-Agent Systems to automate complex … Language Models (LLMs) , Natural Language Processing (NLP) Prompt Engineering , Context Engineering AI Agents , Agentic AI , Multi-Agent Systems LangChain , LangGraph , Model Context Protocol (MCP) RAG , Embeddings , Vector Databases Python , SQL , REST APIs Git , CI/CD Cloud Platforms (Azure, AWS, or GCP) LLM & AI Agent Testing and Evaluation Desirable Skills ...

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 … party services via MCP and related protocols, and define the patterns other engineers build against Implement advanced large language model applications using retrieval-augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integration Build and deliver compelling proofs of concept, live ...

Generative AI & LLM Engineering: Deep, hands‐on expertise in large language models including model selection, fine‐tuning, prompt engineering, retrieval‐augmented generation (RAG), vector database design, and evaluation methodologies. You understand how models behave in production, not just in demos. Agentic Systems Design: Proven … agentic AI systems across priority COO domains, with measurable operational impact (cost, speed, quality, or risk reduction). Launch the shared AI platform — reusable RAG infrastructure, evaluation frameworks, and common tooling adopted across COO Technology. Define and align the multi‐year AI roadmap with COO function leads and the Head ...

practical experience. Machine Learning: Expertise in time‐series forecasting, clustering, and correlation algorithms. Generative AI: Experience with LLM prompting, Retrieval‐Augmented Generation (RAG), and LangChain. Cloud Infrastructure: Deep understanding of AWS, Azure, or GCP infrastructure components Additional Preferred Qualifications: Experience with enterprise observability ...

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 ...

advanced degree preferred. Agentic AI/LLMs: LLM orchestration, prompt engineering, context‐window management and production guardrails; MCP, tool‐augmented agents and RAG at enterprise scale. Enterprise integration: Integrating AI into enterprise systems via APIs and data pipelines, with strong security, data governance and residency practices. Cloud ...

advanced degree preferred. Agentic AI/LLMs: LLM orchestration, prompt engineering, context-window management and production guardrails; MCP, tool-augmented agents and RAG at enterprise scale. Enterprise integration: Integrating AI into enterprise systems via APIs and data pipelines, with strong security, data governance and residency practices. Cloud ...

learn, XGBoost, Hugging Face Transformers). Agentic AI Systems: Hands-on experience with agentic AI setups including LLM orchestration, tool use/function calling, RAG, agents, prompt engineering, or agentic patterns for enterprise workflows. Production ML/LLMOps: Experience with model deployment, monitoring, MLOps/LLMOps, and lifecycle management across ...

solutions, ensuring they're fully understood Strong Python skills and hands‐on experience with ML frameworks and libraries. Experience with Generative AI, LLMs, RAG, and prompt engineering . Strong understanding of MLOps, model deployment, CI/CD, and monitoring. Experience with large datasets, cloud-based data platforms, and ideally Microsoft ...

/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 ...

backend API services, model integration, model monitoring, evaluations and deployments Integrate and optimise LLMs for specific use cases in business planning, including prompt engineering, RAG implementation Build conversational interfaces and agentic workflows that make complex planning tasks accessible through natural language Implement evaluation frameworks to measure and improve GenAI feature ...

experience designing or delivering AI, data, analytics, ML, GenAI, agentic AI, or automation solutions.1+ year of hands-on or solutioning experience with GenAI, LLMs, RAG, AI agents, or enterprise AI platforms. Experience producing solution blueprints, work estimates, delivery plans, staffing models, assumptions, dependencies, SOW inputs, and pricing or commercial inputs. ...

Practical experience implementing components of data pipelines, including real-time streaming tools (AWS Kinesis, Kafka), data orchestration (dbt, Airflow), and managing vector databases for RAG architectures. Observability & Cost Management: SRE/Platform experience with the practical application of real-time monitoring and cloud cost optimisation using native CSP tools ...

Practical experience implementing components of data pipelines, including real-time streaming tools (AWS Kinesis, Kafka), data orchestration (dbt, Airflow), and managing vector databases for RAG architectures. Observability & Cost Management: SRE/Platform experience with the practical application of real-time monitoring and cloud cost optimisation using native CSP tools ...

Practical experience implementing components of data pipelines, including real-time streaming tools (AWS Kinesis, Kafka), data orchestration (dbt, Airflow), and managing vector databases for RAG architectures. Observability & Cost Management: SRE/Platform experience with the practical application of real-time monitoring and cloud cost optimisation using native CSP tools ...

Practical experience implementing components of data pipelines, including real-time streaming tools (AWS Kinesis, Kafka), data orchestration (dbt, Airflow), and managing vector databases for RAG architectures. Observability & Cost Management: SRE/Platform experience with the practical application of real-time monitoring and cloud cost optimisation using native CSP tools ...

Generative AI. Demonstrated ability to architect end‐to‐end Data & AI solutions, with specific expertise in modern Generative AI concepts (e.g., fine‐tuning, RAG, MLOps for LLMs), using either open source and/or ISV tools such as Dataiku, Domino, DataRobot etc. Strong proficiency in a core programming language (e.g. ...

backend API services, model integration, model monitoring, evaluations and deployments Integrate and optimise LLMs for specific use cases in business planning, including prompt engineering, RAG implementation Build conversational interfaces and agentic workflows that make complex planning tasks accessible through natural language Implement evaluation frameworks to measure and improve GenAI feature ...

Practical experience implementing components of data pipelines, including real-time streaming tools (AWS Kinesis, Kafka), data orchestration (dbt, Airflow), and managing Vector Databases for RAG architectures. Observability & Cost Management: SRE/Platform experience with the practical application of real-time monitoring and cloud cost optimisation using native CSP tools ...

architecture for unstructured document ingestion, reasoning, and output generation Design LLM-centric and hybrid AI architectures combining: OCR RAG systems Agentic workflows Design and govern prompt strategies and prompt frameworks for: Loss run and insurance document extraction & normalization Claims summarization, triage, and fraud signal generation Underwriting … platforms Experience integrating AI solutions into enterprise systems .NET (Backend) and React (Frontend) Developer Prompt Engineering & LLM Design Retrieval Augmented Generation (RAG) Architectures Skills and Personal attributes we would like to have: OCR systems for document ingestion and classification AI Governance & Token Economics ...

Trust Layer, Prompt Builder and Agent BuilderExperience integrating Salesforce AI capabilities with enterprise systems through APIs and event-driven architecturesUnderstanding of LLMs, prompt engineering, RAG architectures, and agentic AI frameworksHighly developed problem solving and delivery skills with the ability to analyse complex issues, recommend appropriate solutions and manage projects … experience of: Salesforce AgentforceSalesforce Einstein AIEinstein CopilotAgent BuilderPrompt BuilderSalesforce Data CloudService CloudField ServiceSalesforce FlowApexAI GovernanceResponsible AILarge Language Models (LLMs)Retrieval Augmented Generation (RAG)Prompt EngineeringAutonomous AgentsCRM TransformationWhat's in it for you As well as a competitive salary which is reviewed annually ...

infrastructure required to run AI workloads reliably, moving client architectures from static data lakes to dynamic Vector Databases and Retrieval‐Augmented Generation (RAG) architectures. Agentic Development: Build production‐grade Agentic Workflows and custom AI agents, utilising advanced toolsets to bridge the widening market implementation … maintaining data orchestration toolsets (e.g., dbt, Apache Airflow), ETL/ELT methodologies, real‐time streaming (e.g., AWS Kinesis, Apache Kafka), Vector databases, and RAG architectures. Observability & FinOps: Experience implementing modern observability tooling (OpenTelemetry) alongside automated cost‐control systems (such as Karpenter, Infracost, OpenCost, or Cloud Custodian). Domain & Sector Experience ...

infrastructure required to run AI workloads reliably, moving client architectures from static data lakes to dynamic Vector Databases and Retrieval‐Augmented Generation (RAG) architectures. Agentic Development: Build production‐grade Agentic Workflows and custom AI agents, utilising advanced toolsets to bridge the widening market implementation … maintaining data orchestration toolsets (e.g., dbt, Apache Airflow), ETL/ELT methodologies, real‐time streaming (e.g., AWS Kinesis, Apache Kafka), Vector databases, and RAG architectures. Observability & FinOps: Experience implementing modern observability tooling (OpenTelemetry) alongside automated cost‐control systems (such as Karpenter, Infracost, OpenCost, or Cloud Custodian). Domain & Sector Experience ...

infrastructure required to run AI workloads reliably, moving client architectures from static data lakes to dynamic Vector Databases and Retrieval‐Augmented Generation (RAG) architectures. Agentic Development: Build production‐grade Agentic Workflows and custom AI agents, utilising advanced toolsets to bridge the widening market implementation … maintaining data orchestration toolsets (e.g., dbt, Apache Airflow), ETL/ELT methodologies, real‐time streaming (e.g., AWS Kinesis, Apache Kafka), Vector databases, and RAG architectures. Observability & FinOps: Experience implementing modern observability tooling (OpenTelemetry) alongside automated cost‐control systems (such as Karpenter, Infracost, OpenCost, or Cloud Custodian). Domain & Sector Experience ...

infrastructure required to run AI workloads reliably, moving client architectures from static data lakes to dynamic Vector Databases and Retrieval‐Augmented Generation (RAG) architectures. Agentic Development: Build production‐grade Agentic Workflows and custom AI agents, utilising advanced toolsets to bridge the widening market implementation … maintaining data orchestration toolsets (e.g., dbt, Apache Airflow), ETL/ELT methodologies, real‐time streaming (e.g., AWS Kinesis, Apache Kafka), Vector databases, and RAG architectures. Observability & FinOps: Experience implementing modern observability tooling (OpenTelemetry) alongside automated cost‐control systems (such as Karpenter, Infracost, OpenCost, or Cloud Custodian). Domain & Sector Experience ...