26 to 47 of 47 Retrieval-Augmented Generation Jobs in Scotland

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 ...

prototypes, products and production ready solutions Designing and implementing end to end AI solutions that integrate with enterprise systems Working with LLMs, prompt engineering, RAG patterns, embeddings and fine tuning Developing AI agents and agentic workflows using modern frameworks Working with vector databases, APIs and modern data platforms Supporting … collaborative, high performing environment Experience Required: Strong Python and SQL experience Applied AI engineering, ML engineering or software engineering background Experience with LLMs, RAG, embeddings, prompt engineering or fine tuning Exposure to LangChain, LangGraph, Agent Development Kit or similar agent frameworks Experience with vector databases such as Pinecone, Chroma ...

across ML delivery, backend services, and cloud-native platforms on AWS, applying modern engineering practices and secure coding standards. You will lead development of RAG pipelines, tool-using agents, and evaluation frameworks, partnering with cross-functional teams to drive impactful AI-enabled business workflows while ensuring #J-18808-Ljbffr ...

/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 ...

/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 ...

Experience designing and operating APIs, microservices, distributed systems, and event-driven architectures at enterprise scale. Exposure to Generative AI, large language models, retrieval-augmented generation, semantic search, embeddings, knowledge retrieval, or agentic workflow patterns. Strong focus on software quality, including automated testing ...

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 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 ...

efficiency ; shorter cycle times, less manual effort, higher accuracy, lower cost-to-serve. Engineer the agent core: apply prompt engineering, context engineering, prompt caching, RAG/context-graph retrieval, memory, tool/function calling, MCP integration and multi-agent orchestration. Integrate to standards: connect agents into client ecosystems … Agents SDK, AWS Bedrock AgentCore or Microsoft Agent Framework/Semantic Kernel). Solid command of agent engineering: prompt engineering, context engineering, prompt caching, RAG/context graphs, tool/function calling, MCP, and multi-agent orchestration. Eval-driven development: designing evaluation harnesses and measuring agent quality, safety and reliability. ...

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 ...

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 ...

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 ...

advisor to client CxOs. Key Responsibilities Architecture and Solution Design: Architect enterprise AI solutions using frontier models (Claude, GPT, Gemini family) across agentic automation, RAG, copilots, and multi-modal applications. Pre-Sales and Discovery: Lead technical solutioning during pre-sales and discovery, translating client business challenges into scalable AI architectures … specifically on applied AI/ML or Generative AI. Hands-on GenAI Depth: Proven experience architecting solutions with frontier modelsincluding prompt engineering, fine-tuning, RAG, and agentic/multi-agent system design. LLMOps/MLOps Grounding: Expertise in evaluation frameworks, observability, cost and latency optimization, guardrails, and safe deployment ...

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 ...

Strands Agents Build, integrate, and maintain MCP (Model Context Protocol) servers that expose capabilities to AI agents across client platforms Architect and implement RAG pipelines using Amazon Bedrock Knowledge Bases, managing vector stores, embeddings, and document ingestion from S3 and other sources Apply the A2A (Agent-to-Agent) protocol … integrated MCP servers in a production or near-production context Strands Agents — familiarity with the framework and its application to agentic workflows on AWS RAG implementation — knowledge base design, chunking strategy, vector store configuration, and retrieval evaluation Python — strong, applied proficiency in an AWS and AI context ...

capabilities across all of these services. With our thought leadership and culture of innovation, we apply industry expertise, diverse skill sets and next-generation technology to each business challenge. We believe in inclusion and diversity and supporting the whole person. Our core values comprise of Stewardship, Best People … Azure ML, OpenAI, Langchain, AutoML, OCR, STT, feature stores, vector DBAI/ML architectural patterns and best practices e.g. data drift detection, experimentation tracking, RAG, deployment modelsCloud infrastructure (networking, security, storage, monitoring, scaling, DR/HA) on one or more cloud platformsSW Engineering and DevSecOpsSet yourself apart: Certified cloud Machine ...

capabilities across all of these services. With our thought leadership and culture of innovation, we apply industry expertise, diverse skill sets and next-generation technology to each business challenge. We believe in inclusion and diversity and supporting the whole person. Our core values comprise of Stewardship, Best People … OpenAI, Langchain, AutoML, OCR, STT, feature stores, vector DB AI/ML architectural patterns and best practices e.g. data drift detection, experimentation tracking, RAG, deployment models Cloud infrastructure (networking, security, storage, monitoring, scaling, DR/HA) on one or more cloud platforms SW Engineering and DevSecOps Set yourself apart: Certified ...

capabilities across all of these services. With our thought leadership and culture of innovation, we apply industry expertise, diverse skill sets and next-generation technology to each business challenge. We believe in inclusion and diversity and supporting the whole person. Our core values comprise of Stewardship, Best People … OpenAI, Langchain, AutoML, OCR, STT, feature stores, vector DB AI/ML architectural patterns and best practices e.g. data drift detection, experimentation tracking, RAG, deployment models Cloud infrastructure (networking, security, storage, monitoring, scaling, DR/HA) on one or more cloud platforms SW Engineering and DevSecOps Set yourself apart: Certified ...