22 of 22 Retrieval-Augmented Generation Jobs in Glasgow

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. ...

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 ...

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 ...

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 ...

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 ...

productionize Generative AI and Agentic AI solutions. The ideal candidate brings a balanced mix of modern AI/ML delivery (LLMs/SLMs, RAG, tool-using agents, evaluation, MLOps) and backend/service engineering (Java and/or Python, APIs/microservices, testing, CI/CD, observability, reliability … deploy GenAI and Agentic AI solutions that improve automation, decision-making, and user experience across business workflows. Build LLM/SLM-powered applications including RAG-based systems, summarization/extraction pipelines, chat/coplay experiences, and tool-using agents. Engineer production-grade services using Java and/or Python (REST ...

productionize Generative AI and Agentic AI solutions. The ideal candidate brings a balanced mix of modern AI/ML delivery (LLMs/SLMs, RAG, tool-using agents, evaluation, MLOps) and backend/service engineering (Java and/or Python, APIs/microservices, testing, CI/CD, observability, reliability … deploy GenAI and Agentic AI solutions that improve automation, decision-making, and user experience across business workflows. Build LLM/SLM-powered applications including RAG-based systems, summarization/extraction pipelines, chat/coplay experiences, and tool-using agents. Engineer production-grade services using Java and/or Python (REST ...

/response logging, and safe rollout patterns Preferred Qualifications, Capabilities, and Skills Experience developing generative AI applications, AI agents, vector search, and retrieval-augmented generation patterns Experience building AI agents using frameworks such as LangChain, CrewAI, LangGraph, or similar orchestration platforms Experience operating … integrating serving platforms such as KServe, Ray Serve, NVIDIA Triton Inference Server, Text Generation Inference (TGI), alongside vLLM/llm-d Familiarity with Amazon SageMaker JumpStart, SageMaker Endpoints, and Amazon Bedrock for managed model hosting Experience with online LLM quality monitoring (e.g., hallucination, toxicity, drift detection) and tracing ...

vLLM and llm-d (or directly equivalent model serving stacks) in production Experience developing generative AI applications, AI agents, vector search, and retrieval-augmented generation patterns Experience building AI agents using orchestration frameworks such as LangChain, LangGraph, CrewAI, or similar platforms Experience operating ...

vLLM and llm-d (or directly equivalent model serving stacks) in productionExperience developing generative AI applications, AI agents, vector search, and retrieval-augmented generation patternsExperience building AI agents using orchestration frameworks such as LangChain, LangGraph, CrewAI, or similar platformsExperience operating or integrating model serving ...

learning is essential, with solid NLP and financial knowledge and hands‐on implementation experience. The role focuses on advanced techniques such as semantic search, RAG, NER, prompt engineering, and personalization for content #J-18808-Ljbffr ...

agent workflows, tool-use integrations, and human-in-the-loop controls appropriate for regulated financial services environments Establishes engineering standards for LLM-based applications RAG pipelines, embedding workflows, vector store integrations, and model serving ensuring safety, observability, and reproducibility at scale Drives adoption of advanced technical methods and practices aligned … tools within the work environment) to improve delivery speed, code quality, and operational outcomes at scale (e.g., AI-driven PR review assistance, test generation/maintenance, release readiness checks, incident triage and root-cause acceleration), while defining guardrails for validation, security, resiliency, and reuse across teams. Applies knowledge ...

multi-agent workflows, tool-use integrations, and human-in-the-loop controls appropriate for regulated financial services environmentsEstablishes engineering standards for LLM-based applications — RAG pipelines, embedding workflows, vector store integrations, and model serving — ensuring safety, observability, and reproducibility at scaleDrives adoption of advanced technical methods and practices aligned with … tools within the work environment) to improve delivery speed, code quality, and operational outcomes at scale (e.g., AI-driven PR review assistance, test generation/maintenance, release readiness checks, incident triage and root-cause acceleration), while defining guardrails for validation, security, resiliency, and reuse across teams. Applies knowledge ...

Build and maintain inference-time integrations such as model gateways, APIs, caching, fallbacks, timeouts, and concurrency controls for production AI systems Implement retrieval-augmented generation components where applicable, including chunking, embeddings, retrieval, and grounding strategies Build REST and gRPC endpoints following agreed … cause analysis Leverage enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards Apply knowledge of tools within ...