126 to 150 of 341 Retrieval-Augmented Generation Jobs in the UK excluding London

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Python and experience with AI/ML libraries. Familiarity with cloud infrastructure (AWS preferred) for deploying AI systems. Understanding of RAG (Retrieval-Augmented Generation), vector databases, and memory systems. Experience with RPA or automation tooling is desirable. Knowledge of responsible AI principles ...

preferred), Docker/Kubernetes, CI/CD, and monitoring Strong debugging, collaboration, and continuous-learning mindset Nice to Have: Experience with vector databases and RAG (e.g., OpenSearch, ElasticSearch) Ready to build what's next? Oscar Associates (UK) Limited is acting as an Employment Agency in relation to this vacancy. ...

prompt engineering combined with traditional software/automation engineering. Hands-on expertise utilizing GitHub Copilot, Microsoft Copilot, and Claude Code for code generation, review, and refactoring. Deep proficiency in PowerShell scripting, enterprise automation, and system administration. Strong database background, specifically with SQL Server. Programming proficiency in at least … with Azure AI, OpenAI APIs, or Anthropic APIs. Solid understanding of CI/CD, DevOps, and enterprise automation platforms. Familiarity with RAG (Retrieval-Augmented Generation) architectures, vector databases, and knowledge systems. Previous experience deploying AI solutions within large-scale or regulated enterprise environments. ...

systems Driving adoption of Agentic AI across multiple engineering teams Defining reusable engineering standards, architectures and implementation patterns Designing and evolving production RAG and retrieval architectures Establishing effective LangGraph/LangChain patterns for agentic applications Improving AI evaluation, testing, observability and production monitoring Developing guardrails, controls and approaches … Python and strong software engineering fundamentals Production Machine Learning/AI engineering AWS Agentic AI and LLM-based applications Retrieval Augmented Generation (RAG) LangChain and/or LangGraph Vector databases and retrieval MLOps and production deployment AI evaluation, testing and observability ...

automated testing with pytest • Experience supporting and improving large, established production codebases, with a strong understanding of secure software development Experience with LangChain, RAG, vector databases, Azure, Entra ID, OAuth/OIDC, Redis, Terraform, Helm, Prometheus, Grafana or OpenTelemetry would be valuable but is not essential. Current SC clearance ...

cloud‐native delivery approaches Curiosity about AI‐enabled systems and how they scale responsibly Experience with LLMs, AI agent orchestration, or tools such as RAG, vector databases or LangChain is a bonus, but not essential. What's in it for you Flexible hybrid working 25 days leave, 2 days paid ...

+ security considerations). Be the AI champion/evangelist within the tech team. You will set reference architectures for predictive ML, generative AI, RAG, and agentic systems; choose platforms and patterns teams can reuse; and hold the line on quality, governance, and operational readiness. The role requires strategic thinking ...