15 of 15 Retrieval-Augmented Generation Jobs in Scotland

Solutions Engineer Job Overview Design, develop and deploy AI solutions within Microsoft Azure environments Deliver Retrieval-Augmented Generation (RAG), forecasting, anomaly detection and data classification solutions Assess existing business systems and identify opportunities for AI-driven improvements Integrate AI technologies with Microsoft 365 applications … frameworks such as Haystack, LlamaIndex or Semantic Kernel Familiarity with vector databases and Retrieval-Augmented Generation (RAG) architectures Experience integrating Microsoft 365 services using Graph API Strong analytical and problem-solving abilities Ability to balance innovation and experimentation with practical business delivery Excellent communication ...

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 environment Python, 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 for Proven enterprise AI or ML architecture design, strong cloud ...

Design and build AI-enabled platforms and intelligent workflows using large language models (LLMs), retrieval-augmented generation (RAG) pipelines and embedding models. Develop scalable backend services and AI integrations, working closely with engineering, architecture and business teams, and support deployment, governance and operational rollout … infrastructure exposure, plus experience in Financial Services or another regulated industry, is essential. Tech skills include Python, SQL, LLM development (prompt engineering, fine-tuning, RAG), LangChain/LangGraph, agent frameworks, vector databases, FastAPI, and AWS, Azure, GCP or Databricks. CI/CD, MLOps and LLMOps familiarity expected. Strong communication ...

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 or Azure OpenAI Experience ...

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 ...

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 ...

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 ...

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 ...

technologies Experience architecting scalable AI and ML solutions across serverless, containerised or GPU enabled environments Experience with LLMs, prompt engineering, embeddings, semantic search and RAG patterns Exposure to vector databases and agent frameworks such as LangChain, LangGraph or similar technologies Understanding of MLOps, LLMOps and model lifecycle management principles Experience ...

including relevant AI, data, integration and deployment services. Desirable skills and experience:? Experience with agentic AI patterns, orchestration frameworks, retrieval augmented generation, prompt engineering and responsible AI practices. Knowledge of data architecture, integration patterns, APIs, event-driven design and enterprise platforms used to connect ...

detection that balance performance, explainability, and operational risk. Lead a team of Data Scientists. Drive innovation in LLM-based security automation, including prompt engineering, RAG pipelines, and fine‐tuning of models. Collaborate with Data Engineers to build scalable, secure, and production‐ready ML pipelines on GCP. Partner with cyber defence ...

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 ...

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 ...

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 ...

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 or 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 via OpenTelemetry ...