1 to 25 of 68 Permanent Retrieval-Augmented Generation Jobs in the UK excluding London

focused on building the intelligence layer of advanced AI products. You will design and implement Retrieval-Augmented Generation (RAG) pipelines, agentic workflows, and robust evaluation frameworks that ensure AI systems are accurate and aligned with real-world business needs. Reporting to the Director … with AI Platform Engineering to ensure solutions are scalable, maintainable, and production ready. Design, build, and train production-grade generative AI systems. Implement robust RAG pipelines that effectively leverage proprietary and domain-specific content. Develop agentic and workflow-based AI patterns with a focus on determinism, quality, and maintainability. Apply ...

optimise pipelines for data collection, preprocessing, and model evaluation. Collaborate with product engineers to deploy models into scalable production systems. Experiment with prompt engineering, RAG architectures, and multimodal models. Contribute to internal tools for monitoring, testing, and improving AI performance. Stay on the edge of ML/AI research … Intelligence, Deep Learning, NLP, Natural Language Processing, Large Language Models, Generative AI, GenAI, Neural Networks, PyTorch, TensorFlow, Hugging Face, OpenAI, LangChain, RAG, Retrieval-Augmented Generation, Python, Data Science, AI Research, MLOps, Data Pipelines, Prompt Engineering, Model Fine-Tuning, Cloud Computing, AWS, Azure, Google Cloud ...

Level: Junior–Mid Overview: As a Developer Advocate focused on AI/GenAI, you will empower AI builders with practical LLM/RAG samples, vector search demos, and hands-on tutorials. You'll foster engagement and growth in the AI developer community while ensuring GitHub advocacy repositories remain organized, discoverable … date. Key Responsibilities: Build AI/GenAI sample applications, including prompt pipelines, embeddings, retrieval-augmented generation (RAG), and evaluation/reference implementations. Maintain GitHub repositories and automation for AI assets; standardize repo structure, documentation, and contribution models. Produce step-by-step tutorials, quickstarts ...

design, build, and deliver next-generation AI systems. You will work across LLMs, retrieval-augmented generation (RAG), and modern agent frameworks to transform large, unstructured data into meaningful insights and production-ready capabilities. This is a hands-on role within a growing … entity extraction, and intelligent automation. Design scalable data and streaming pipelines capable of handling large, heterogeneous datasets. Build and optimize vector search, embeddings, and RAG systems to support high-quality retrieval. Deliver production-ready APIs, services, and model inference systems. Manage deployment, monitoring, observability, and continual improvement of ML models. ...

that power all internal GenAI product squads. You will be instrumental in designing and implementing our award winning Generative AI Platform Model Orchestration Layer, RAG infrastructure, communications layer, agentic layer and centralised governance/safety guardrails etc. This is a hybrid role, typically working 1 day per week … access to LLMs and other generative models. Engineer and maintain production-ready Vector Database and Retrieval-Augmented Generation (RAG) infrastructure, including high-throughput indexing pipelines and efficient retrieval strategies for enterprise data. Develop and manage a standardised, secure Agent Framework/ ...

driven solutions for Test Automation within the Securities Processing domain. The ideal candidate will work on building innovative AIML solutions for Test Case Generation, Test Prioritization, Defect Triage, Reporting, Code Coverage, and Automation Framework Migration/Setup . AIML concepts, LLMs, Agentic AI, Python, and modern AI workflows. … , Test Prioritization, Defect Triage, and Code Coverage analysis . Develop Generative AI and Retrieval-Augmented Generation (RAG) –based workflows. Work with Large Language Models (GPT, Claude, etc.) for domain-specific problem solving. Implement Agentic AI workflows , including copilots, autonomous AI agents ...

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 Full Stack applications for both GenAI and non-GenAI ...

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 Full Stack applications for both GenAI and non-GenAI ...

CrewAI, AutoGen, or equivalent internal frameworks). Practical experience in connecting agents and products to Retrieval-Augmented Generation (RAG) systems, understanding vector stores, chunking strategies, and retrieval performance. You will be connecting to our internal Gen AI Platform's vector databases. Experience ...

data processing frameworks (e.g. Apache Spark). Knowledge of Large Language Model (LLM) integration, Retrieval-Augmented Generation (RAG), or Agentic AI frameworks. Experience with high-performance computing (HPC) environments (e.g. SLURM), including use of GPU nodes for accelerating large-scale or AI-driven pipelines. ...

environments. This is an end-to-end role focusing on the design and implementation of applied AI capabilities, from building secure knowledge-based chat (RAG solutions) over internal documentation and codebases, to developing complex agentic workflows that integrate deeply within their product suite. Key Responsibilities Define and execute … core business objectives. Implement documentation ingestion pipelines for structured knowledge bases. Build secure, enterprise-grade Retrieval-Augmented Generation (RAG) systems. Collaborate with engineering teams to embed AI features into existing and new products. Design and deploy agentic workflows to automate multi-step tasks across ...

regulated environment, ensuring adherence to AI governance and compliance frameworks. Design and implement Retrieval-Augmented Generation (RAG) architectures to optimize information retrieval for LLMs. Mentor junior members of the development team, providing expert support when required. Contribute to design decisions ...

building and deploying applied AI systems, with a particular focus on large language models (LLMs), retrieval-augmented generation (RAG), and conversational AI. This is a hands-on role operating across the full AI lifecycle, from experimentation and prototyping through to production deployment. … customer impact. Key Responsibilities Leading the development of AI proof-of-concepts and production systems, including LLM-powered assistants and chatbots. Designing and implementing RAG pipelines using vector search, embeddings and knowledge retrieval strategies. Fine-tuning, evaluating and deploying language models, with a focus on response quality, reliability ...

solutions. Define product requirements, user stories, acceptance criteria, and success metrics for AI features. Oversee the life cycle of AI models (classification, extraction, summarization, RAG, LLM fine-tuning, etc.), including data sources, training needs, evaluation standards, and human-in-the-loop workflows. Ensure model performance meets legal-grade quality, reliability … discovery platforms, CLM systems, or legal research technologies. Familiarity with prompt engineering, model evaluation frameworks, retrieval-augmented generation (RAG), and fine-tuning techniques. Experience with responsible AI frameworks or compliance-sensitive software. Background working with structured legal data, ontologies, or knowledge graphs. Success Indicators ...

build and evolve Generative AI (GenAI) proof-of-concepts (POCs) for clients using techniques like Retrieval-Augmented Generation (RAG) and intelligent agents. Support the transition of these POCs into scalable, production-ready solutions. Contribute to the design and development of full-stack applications ...

fast‐paced environment Desirable Experience: Experience with generative AI and large language models, including LangChain, vector databases and retrieval‐augmented generation Knowledge of AutoML, hyperparameter optimisation and advanced MLflow features. Experience working with streaming or real‐time data platforms Familiarity with CI/ ...

based systems (e.g. LangChain, LlamaIndex, AutoGen, custom agent frameworks) Familiarity with prompt engineering, tool calling, retrieval-augmented generation (RAG), and multi-agent workflows Experience integrating AI into real systems, APIs, event-driven architectures, data pipelines, and automation platforms (e.g. UiPath, Blue Prism) Comfortable experimenting ...

Implementing MLOps foundations including CI/CD, automated testing, monitoring, drift detection and model versioning Building generative AI features such as RAG pipelines, embeddings, and AI agents Developing clean, testable code and APIs using modern engineering practices Ensuring technical delivery aligns with security, governance, architecture and enterprise standards Evaluating ...

related field. 5+ years of experience in AI consulting, solution architecture, or applied machine learning. Strong understanding of generative AI models, prompt engineering, and RAG pipelines. Familiarity with cloud platforms (AWS, Azure etc) and enterprise integration patterns. Excellent communication and stakeholder management skills. Company Overview Element ...

from experimentation into live systems Experience with MLOps tools (MLflow, Weights & Biases, DVC, or similar) Hands-on exposure to LLM/GenAI applications, including RAG Nice to have: Experience with advanced LLM techniques (e.g. agents, tool use) Familiarity with vector databases and feature stores Experience with Docker, Kubernetes, or Infrastructure ...

Machine Learning, and the wider Microsoft ecosystem. Develop agentic workflows using frameworks such as Autogen, Swarm, or LangGraph. Design and implement RAG (Retrieval-Augmented Generation) systems and integrate them into business processes. Integrate OpenAI GPT models, embeddings, and vector stores into production-grade solutions. … What We're Looking For Strong experience building AI/LLM solutions on Azure AI, Azure ML, or similar cloud environments. Practical experience implementing RAG pipelines, embeddings, and vector search. Exposure to agent-based architectures and workflow orchestration. Solid Python engineering skills, including familiarity with ML frameworks and LLM toolchains. ...

engineering experience Strong experience with modern GenAI/LLM frameworks: LangChain, LangGraph, PyTorch, etc. Comfort working with unstructured data: entity extraction, semantic search, vectorisation, RAG-like patterns Awareness of data engineering: pipelines, legacy data extraction, interfacing with data teams Experience or strong curiosity working with Azure and Azure Databricks ...

Permanent Required Experience of architecting and solutioning in Gen AI, Agentic AI, classic ML and automation space Experience and good understanding of Prompt engineering, RAG pipelines, Supervised/unsupervised Model tuning, MLOps/LLMOps pipelines, and AI observability Experience in Enterprise-grade RAG-based solutions with LLMs (OpenAI, Hugging Face ...

tools, and enterprise connectors. Skills: 6–8 years in Software/application engineering, AI engineering, or applied data engineering. Strong experience with LLMs, embeddings, RAG, retrieval stacks, and vector stores. Hands-on experience in multi-agent systems, agent orchestration, or MCP-like tool patterns. Proficiency in Python, including ...

high-impact projects while building and leading a team from the ground up. Key Skills/Experience: Experience with LLMs, generative AI models, and RAG pipelines. Frameworks: OpenAI, Anthropic Claude, Gemini, and open-source models Agentic AI, Langgraph, Crew AI, Autogen Python Development (Hands-on coding) Must have deployed ...