1 to 25 of 47 Retrieval-Augmented Generation Jobs in Scotland

Java, including experience with distributed data processing. Leverage enterprise-authorized AI coding assistant tools to improve code quality and delivery speed (e.g., code generation/refactoring, unit tests, documentation), while validating outputs via peer review, automated testing, and secure coding standards; contribute reusable patterns and learnings to improve … Google Bard (or similar). Exposure to LLM application patterns and tooling, including LangChain, retrieval-augmented generation (RAG), and fine-tuning. Experience communicating with and engaging senior leadership and other stakeholders. Preferred: experience building and supporting end-to-end AWS applications, including batch ...

with the team to develop GenAI proof-of-concepts (POCs) for clients using technologies like Retrieval-Augmented Generation (RAG) and intelligent agents.* Scale existing POCs to production-ready solutions for customer use.* Design and develop FullStack applications for both GenAI and non-GenAI projects. ...

architecture strategy and roadmap. Build scalable AI/ML and Agentic AI platforms spanning LLMs, retrieval-augmented generation (RAG), embeddings and semantic search. Lead on deployment, governance and integration patterns, advise clients on platform capability and implementation strategy, and partner with engineering, data, DevOps … business teams to deliver end-to-end solutions. Tech environmentPython, LLMs (prompt engineering, fine-tuning, RAG), LangChain/LangGraph, agent frameworks, vector databases, MLOps/LLMOps, AWS, Azure, GCP, Databricks, Kubernetes and GPU infrastructure. What we're looking forProven enterprise AI or ML architecture design, strong cloud and modern data ...

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns … safe and effective usage within team practices. Working knowledge of LLM application patterns - prompt design, retrieval-augmented generation (RAG), embeddings and vector search, structured output, and tool/function calling. Experience with agentic AI frameworks - multi-agent orchestration, planning and tool use, and integration ...

from the ground up, with significant scope for ownership, innovation and career development. Key Responsibilities Design and deliver AI solutions using Microsoft Azure, including RAG, data classification, forecasting and anomaly detection. Identify opportunities to introduce AI into existing business processes and systems. Integrate AI solutions with Microsoft 365, SharePoint, Teams … take AI projects from prototype through to robust production deployment. Desirable Skills Experience with Retrieval-Augmented Generation (RAG). Knowledge of vector databases. Experience with AI frameworks such as Haystack, LlamaIndex or Semantic Kernel. Experience with forecasting, anomaly detection or data classification. Knowledge ...

multi‐modal large language models across text, image, audio, and video Design and implement agentic workflows using prompt engineering, Retrieval‐Augmented Generation (RAG), APIs, and enterprise data integrations Fine‐tune, optimize, and deploy large language and multi‐modal models with a focus on scalability … within agile delivery environments Bonus Points For Experience building AI agent frameworks or autonomous orchestration platforms Knowledge of vector databases, semantic search, and advanced RAG architectures Experience with cloud platforms such as AWS, Azure, or GCP for AI workloads Familiarity with financial services, banking, or regulated enterprise environments Exposure ...

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, and agentic frameworks Practical experience with CI/CD, containerisation, and cloud-native deployment patterns Demonstrated experience delivering system design, application development, testing ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Snowflake Platform including Cortex, Horizon and Snowpark. Advanced proficiency in Azure, AWS, or GCP platform setup and administration. Practical understanding of Generative AI, RAG frameworks, Agentic AI architectural patterns, and security guardrails. Solid understanding of data governance standards (lineage, access control) and performance/cost optimization frameworks. Job responsibilities Lead ...

agentic AI workflows that automate and streamline complex business processes. Design and apply advanced techniques such as semantic search, retrieval‐augmented generation (RAG), named entity recognition (NER), prompt engineering, and personalization for content extraction, search, question answering, reasoning, and recommendation. Develop ...

/ML Lead Engineer in Glasgow to design, build, and productionize Generative AI and Agentic AI solutions. You will deliver GenAI apps using LLMs, RAG, and tool-using agents on AWS and cloud-native platforms. The role emphasizes modern AI engineering workflows, secure coding, observability, and collaboration with cross-functional ...

qualifications, capabilities, and skillsExposure to cloud technologies like AWS/Azure/GCPExposure to LLM models like ChatGPT, Google Bard etc. Exposure to Langchain, RAG and Fine Tuning of the LLM Models. Exposure to PySpark and Databricks for Data pipeline creation. Familiarity with modern front-end technologiesExposure to cloud technologiesExperience ...

scale in production environments and create capacity for our business partners. From AI-powered investment insights and risk management solutions to next-generation agentic workflows - our work is helping to shape the future of BlackRock. Our teams work on some of the firm's highest priority strategic initiatives … standards such as Model Context Protocol (MCP) for connecting agents to tools and data. Experience with retrieval systems for agents: retrieval-augmented generation, embedding models, vector databases, and long-term memory. Experience with machine learning libraries (e.g. PyTorch, Tensorflow), cloud platforms ...

scale in production environments and create capacity for our business partners. From AI-powered investment insights and risk management solutions to next-generation agentic workflows - our work is helping to shape the future of BlackRock. Our teams work on some of the firm's highest priority strategic initiatives … standards such as Model Context Protocol (MCP) for connecting agents to tools and data. Experience with retrieval systems for agents: retrieval-augmented generation, embedding models, vector databases, and long-term memory. Experience with machine learning libraries (e.g. PyTorch, Tensorflow), cloud platforms ...

scale in production environments and create capacity for our business partners. From AI-powered investment insights and risk management solutions to next-generation agentic workflows - our work is helping to shape the future of BlackRock. Our teams work on some of the firm's highest priority strategic initiatives … standards such as Model Context Protocol (MCP) for connecting agents to tools and data. Experience with retrieval systems for agents: retrieval-augmented generation, embedding models, vector databases, and long-term memory. Experience with machine learning libraries (e.g. PyTorch, Tensorflow), cloud platforms ...