1 to 25 of 125 Permanent Retrieval-Augmented Generation Jobs in London

implemented responsibly and effectively. This includes shaping initiatives involving: Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) Agentic AI systems and multi-agent architectures AI-assisted decision systems Generative AI applications embedded within enterprise workflows You will work across the full lifecycle … through industry hype to focus on practical outcomes Solution Development Help shape AI-enabled products and solutions using modern architectures including: LLM-based applications RAG pipelines AI orchestration layers agent-based systems Work closely with data scientists, ML engineers, and data engineers to move ideas from concept to implementation. Evangelism ...

multi-agent collaboration). AI Architecture: Deep understanding of AI/ML system architecture patterns, including microservices, event-driven architectures, and patterns specific to RAG (Retrieval-Augmented Generation), Graph RAG, Agentic RAG, and multi-agent systems. Data: Knowledge of industry approaches to data engines … data labeling like Scale.ai and Mercor. Experience with auto data-labelling and synthetic data generation techniques ...

agency. This role presents an opportunity to engage deeply with MLOps, vector databases, and Retrieval-Augmented Generation (RAG) pipelines. If you are passionate about shaping the future of AI and thrive on complex, high-impact challenges, we encourage you to apply. What … cost-effectiveness. Power Generative AI: Develop and manage specialized data flows for generative AI applications, including integrating with vector databases and constructing sophisticated RAG pipelines. Champion Data Governance & Ethical AI: Implement best practices for data quality, lineage, privacy, and security, ensuring our AI systems are developed and used responsibly ...

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 ...

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 ...

delivery of AI and cloud-native architectures, including Generative AI, NLP, and LLM solutions. Retrieval-Augmented Generation (RAG) and CAG (Cache Augmented) Architecture: Defining architectural patterns for end-to-end pipelines Act as a trusted advisor to senior stakeholders, guiding AI roadmaps ...

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 ...

discuss flexible working! Top 3 skills needed for this role: Expert AI/ML Skills – Strong in both traditional ML and modern LLM/RAG/agentic workflows Production Deployment & MLOps – Comfortable shipping scalable AI systems using APIs, microservices, Docker/Kubernetes, and cloud (GCP ideal) Business Partnering – Able … based applications. The role involves building internal and external AI solutions such as Retrieval-Augmented Generation (RAG) systems, chatbots, and voice-based customer service platforms, all deployed on Google Cloud Platform (GCP). You will work closely with data scientists, software engineers, and product ...

discuss flexible working! Top 3 skills needed for this role: Expert AI/ML Skills Strong in both traditional ML and modern LLM/RAG/agentic workflows Production Deployment & MLOps Comfortable shipping scalable AI systems using APIs, microservices, Docker/Kubernetes, and cloud (GCP ideal) Business Partnering Able … based applications. The role involves building internal and external AI solutions such as Retrieval-Augmented Generation (RAG) systems, chatbots, and voice-based customer service platforms, all deployed on Google Cloud Platform (GCP). You will work closely with data scientists, software engineers, and product ...

managing multi-API workflows using tools like LiteLLM to ensure flexibility and resilience. Building sophisticated Retrieval-Augmented Generation (RAG) systems, leveraging advanced techniques like embeddings with Voyage AI, rerankers , and query enrichment . Designing and maintaining efficient data pipelines and vector storage solutions using … PyTorch. Proven experience building and deploying applications with LLM APIs such as OpenAI , Anthropic , Gemini , and DeepSeek . Hands-on experience with the full RAG pipeline, including vector embeddings , rerankers , and data indexing in databases like MongoDB. Practical knowledge of LLM fine-tuning, prompt engineering, and performance optimization. Familiarity with ...

architecture and hands-on delivery across Generative AI, Large Language Models (LLMs), Agentic AI and Retrieval-Augmented Generation (RAG). Key Responsibilities: Lead AI strategy and executive advisory engagements Architect enterprise-grade Generative AI and Agentic AI solutions Design multi-agent systems, orchestration frameworks … RAG pipelines Own end-to-end solution architecture from discovery to implementation Shape AI-led deal pursuits, presales and business cases Define AI operating models, governance and responsible AI frameworks Integrate AI solutions with ERP, CRM and enterprise platforms Required Experience: Strong consulting background in AI/digital transformation Deep ...

investment banking, asset management, or capital markets domains.* Deep expertise in ML, NLP, LLMs, retrieval-augmented generation (RAG), embeddings, and modern MLOps practices.* Strong experience working with both structured financial datasets and unstructured data sources (e.g., filings, call transcripts, research).* Familiarity with front ...

monitoring. Start date: February 2026 Responsibilities: Developing new applications to support marketing performance and automation Build AI-powered prototypes and applications using LLMs, LangChain, RAG pipelines, and other emerging frameworks. Experiment with new AI technologies (e.g. vector databases, embedding models, prompt optimisation, AI agent, Google AI Studio/Gemini ...

create high-quality pre-trained datasets, fine-tuned industry-specific Large Language Models(LLMs), and Retrieval-Augmented Generation (RAG) pipelines supported by vector databases. Our innovations can reduce Generative Artificial Intelligence(Gen AI) costs by up to 80% and bring Gen AI solutions ...

realisation across complex technology estates. Key Responsibilities AI Engineering & Delivery Design, build, and deploy production‐grade AI/ML solutions , including: LLM‐enabled applications (RAG, agents, copilots) Predictive models and intelligent automation AI services embedded into modernised platforms Own end‐to‐end AI lifecycle : discovery, PoC, productionisation, monitoring, and optimisation … least one of: TensorFlow, PyTorch, scikit‐learn Practical experience with LLMs and Generative AI , including: Prompt engineering Retrieval‐Augmented Generation (RAG) Agentic workflows Experience deploying AI on cloud platforms (Azure, AWS or GCP) Containerisation and orchestration (Docker, Kubernetes) M&A & Enterprise Transformation Proven experience ...