1 to 25 of 136 Permanent Retrieval-Augmented Generation Jobs in London

Senior AI Engineer (Gen AI & RAG) Greater London, England, United Kingdom (On-site) Join an award-winning, internationally recognised B2B Consultancy as a Senior AI Engineer, where you will play a central role in delivering and evolving production-grade generative AI capabilities that power real-world business outcomes. This … intelligence layer of next-generation AI products. You will architect and implement Retrieval-Augmented Generation (RAG) pipelines, agentic workflows, and rigorous evaluation frameworks that ensure AI systems are accurate, reliable, and grounded in genuine business value. Reporting to the Director ...

solutions for documentation, SDKs, configurations and pipelines to enable rapid deployment of GenAI applications (including Retrieval-Augmented Generation (RAG) pipelines) and agents with planning, memory, and workflow orchestration Implement tools and frameworks for model versioning, experiment tracking, and lifecycle management Develop systems to monitor … secure mission-critical applications Previous experience deploying and managing LLM-model based applications and agents Exposure to vector stores such as Pinecone, GCP RAG engine, and AWS S3 Vector Buckets Exposure to cloud-native microservices architecture Familiarity with advanced AI/ML concepts and protocols, including Retrieval-Augmented ...

Technologies team. The candidate must possess deep expertise in Generative and Agentic AI, including LLMs, retrieval-augmented generation (RAG), machine learning, and interoperability standards such as the Model Context Protocol (MCP). The role involves shaping the Generative AI strategy, selecting suitable models … through modular, reusable skills and resources; defining standards for custom tools, connectors and skills ensuring reliable, secure, and consistent operation. Retrieval‐Augmented Generation (RAG) & Knowledge Architecture – expertise in RAG and knowledge‐grounded systems, including chunking, embeddings, vector databases (Pinecone, Weaviate, Chroma, pgvector, FAISS), hybrid ...

defined metrics. This role requires strong technical expertise across Generative and Agentic AI—including LLMs, retrieval-augmented generation (RAG), autonomous and multi-agent systems, and modern interoperability standards such as the Model Context Protocol (MCP)—coupled with excellent communication skills to engage with clients … agents load on demand. Ability to design custom tools, connectors, and skills enabling specialized, domain‐specific tasks reliably and safely. Retrieval‐Augmented Generation (RAG) & Knowledge Systems : Proven experience designing RAG and knowledge‐grounded systems, including chunking strategies, embeddings, vector databases (Pinecone, Weaviate, Chroma, pgvector ...

maintain data pipelines and curated datasets supporting AI, analytics, reporting and automation use cases. Develop AI-enabled solutions using generative AI, retrieval-augmented generation, predictive analytics, workflow automation and agentic AI. Build secure integrations between internal systems, databases, APIs, document repositories, market data providers … Power Query or similar reporting tools. Hands-on exposure to AI and machine learning technologies, which may include: LLM APIs Prompt engineering Retrieval-augmented generation Agentic workflow frameworks Python machine learning libraries Model testing and evaluation Understanding of the full solution life cycle, including ...

evolve the enterprise AI platform that powers AI products across the company. Develop scalable AI services supporting Agentic AI, Retrieval‐Augmented Generation (RAG), embeddings, vector search, model serving, and intelligent automation. Build orchestration layers that transform structured and unstructured enterprise data into reusable … Hands‐on experience building applications powered by Large Language Models (LLMs). Experience building Model Context Protocol (MCP) and Retrieval‐Augmented Generation (RAG) solutions, making use of embeddings, vector databases, and modern LLM orchestration frameworks. Experience building or contributing to agentic AI systems, intelligent ...

using Python, machine learning frameworks, and Generative AI/LLM components. Pipelines: Develop and optimize Retrieval-Augmented Generation (RAG) pipelines, vector databases, and scalable data processing streams. MLOps: Implement model deployment, monitoring, testing, and lifecycle management using standard CI/CD and containerized workflows. … role. Core Tech: Solid professional experience with Python and frameworks like PyTorch, TensorFlow, or Scikit-Learn. GenAI: Hands-on experience developing LLM-based applications, RAG pipelines, and prompt engineering. Infrastructure: Experience deploying applications in cloud environments (AWS, Azure, or GCP) using Docker and Kubernetes. Engineering: Strong software engineering fundamentals, including ...

pipelines, distributed training frameworks, inference/serving platforms, MLOps/LLMOps tooling, vector databases, and retrieval-augmented generation (RAG) and agentic architectures. Define and AI governance frameworks covering model risk management, responsible AI, data lineage, bias/fairness testing, explainability, and regulatory alignment … Kubeflow, MLflow, and at least one hyperscaler ML platform (SageMaker, Azure ML, or Vertex AI). Generative AI: LLM fine-tuning (LoRA/QLoRA), RAG architecture design, vector databases (Pinecone, Milvus, Weaviate), and agentic frameworks (LangChain, LangGraph, Semantic Kernel). Data pipelines: data lake/lakehouse architectures, ETL/ ...

Build scalable AI/ML and Agentic AI platforms, working across large language models (LLMs), retrieval-augmented generation (RAG), embeddings, and semantic search. Support AI deployment, governance, and integration patterns, and advise clients on platform capability and implementation strategy. Collaborate closely with engineering, data … DevOps, and 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 experience designing enterprise AI or ML architectures ...

engineers who have built AI solutions around document intelligence, enterprise search, knowledge management, NLP or Retrieval-Augmented Generation (RAG), rather than purely research-focused AI. Key Responsibilities Design, build and deploy production-ready LLM and Machine Learning solutions using Azure Databricks, Azure Machine Learning … Foundry Good understanding of MLOps and LLMOps Excellent Python engineering skills with experience using frameworks such as LangChain, LangGraph, PyTorch and Pydantic Experience with RAG, vector search, embeddings, prompt engineering and enterprise retrieval architectures Experience building AI solutions within document-heavy environments such as legal, financial services, insurance ...

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 ...

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 ...

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 ...

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 ...

Expertise in time-series forecasting, clustering, and correlation algorithms.* Generative AI: Experience with LLM prompting, Retrieval-Augmented Generation (RAG), and LangChain.* Cloud Infrastructure: Deep understanding of AWS, Azure, or GCP infrastructure components**Additional Preferred Qualifications:*** Experience with enterprise observability and AIOps platforms such ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

world impact. This position sits within a growing AI function and will focus heavily on Retrieval-Augmented Generation (RAG), agentic workflows, evaluation frameworks, and LLM optimisation. Key Responsibilities: Design and develop scalable RAG pipelines and generative AI workflows Build solutions using LangChain, LangGraph … contribute to technical best practices Key Requirements: Strong Python engineering experience Commercial experience building production-grade AI/ML systems Hands-on experience with RAG architectures and LLM workflows Experience with LangChain, LangGraph, and/or Llama Index Understanding of evaluation methodologies for generative AI Experience working within scalable cloud ...

agentic architectures. Good understanding of modern AI concepts and practices, including: Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) Prompt Engineering AI Guardrails AI Evaluation Frameworks Experience with AI frameworks such as Agent Development Kit (ADK), LangChain, LangGraph, CrewAI, or similar. Experience working with ...

enterprise environment. Driving build versus buy evaluations and recommending appropriate technologies. Delivering solutions including retrieval-augmented generation (RAG), agentic AI, automation, and intelligent decision-support tools. Working closely with engineering, security, governance, and business stakeholders. Navigating complex and ambiguous problem spaces to identify practical … fundamentals and experience building scalable applications. Experience making architecture and technology decisions within enterprise organisations. Strong knowledge of modern AI engineering practices, including LLMs, RAG, agentic workflows, and MLOps concepts. Experience working with cloud platforms, ideally AWS. Ability to balance innovation with risk, governance, and operational requirements. Strong stakeholder management ...

solution approaches across the modern AI stack, including Large Language Models (LLMs), Prompt Engineering, Retrieval-Augmented Generation (RAG), Vector Databases and MLOps. Support clients in navigating cloud, security, data governance and operating model considerations required for enterprise AI adoption. Communicate complex machine learning ...