1 to 25 of 133 Permanent Retrieval-Augmented Generation Jobs in London

/state handling, and fallback logic Implement and consume vector store integrations , prompt templates, and retrieval-augmented generation (RAG) techniques Ensure workflows are robust, secure, and observable , with proper logging, monitoring, and exception handling Partner with Product Managers, SMEs, and QA to translate business … monitoring tools to track agent performance. Exposure to knowledge graphs, memory management systems, or retrieval-augmented generation (RAG) pipelines. ...

/state handling, and fallback logic Implement and consume vector store integrations , prompt templates, and retrieval-augmented generation (RAG) techniques Ensure workflows are robust, secure, and observable , with proper logging, monitoring, and exception handling Partner with Product Managers, SMEs, and QA to translate business … monitoring tools to track agent performance. Exposure to knowledge graphs, memory management systems, or retrieval-augmented generation (RAG) pipelines. ...

Engineer – RAG & Knowledge Systems Onsite (London, UK) | Hybrid Option Available Experience: 3+ years AI | Generative AI | Knowledge Systems About the Role We’re looking for an LLM Engineer to design and implement retrieval-augmented generation (RAG) systems powering next-generation AI applications. … knowledge and document systems Retrieval pipelines combining: embeddings vector databases LLM inference Production-grade generative AI workflows Key Responsibilities Design and implement RAG architectures and retrieval pipelines Develop systems using LLMs, embeddings, and vector databases Optimise relevance, latency, and response quality of AI systems Integrate ...

delivering AI/ML or generative AI systems in production Strong understanding of LLMs , including: Retrieval-Augmented Generation (RAG) Experience building or delivering agentic AI systems Strong Python engineering skills and experience building API-based services Experience with modern data architectures and system design ...

generative AI systems in production Deep understanding of LLMs , including: Prompt engineering Embeddings Fine-tuning Retrieval-Augmented Generation (RAG) Demonstrated experience building and scaling agentic AI systems Strong experience with AI system design, architecture, and distributed systems Expertise in API-based backend development (e.g. ...

Data & AI, Index Engineering, and Index Product teams to explore how large language models (LLMs), retrieval-augmented generation (RAG), and agentic systems can transform how MSCI designs, evaluates, and communicates index methodologies. The team operates at the intersection of AI research, applied engineering … domain expertise to shape the next generation of MSCI Index capabilities. Your Key Responsibilities: Design and develop advanced AI systems leveraging LLMs, RAG, and agentic architectures to enhance index research, methodology interpretation, and automation. Prototype and validate new AI concepts, translating research ideas into working, production-ready solutions. ...

design and deploy intelligent AI systems powered by: Large Language Models (LLMs) Agentic AI frameworks Retrieval-Augmented Generation (RAG) Multi-agent orchestration Tool-using autonomous workflows This is a hands-on engineering role focused on production delivery, scalability, reliability, and business impact. Your Work … Will Include Building AI agents with reasoning, planning, memory, and tool orchestration Developing advanced RAG pipelines and context-aware AI systems Designing MCP-style architectures and interoperable AI workflows Creating recommendation, forecasting, and classification models on large-scale datasets Automating complex business operations using AI-driven decision systems Integrating ...

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 ...

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 ...

emerging AI/ML capabilities within modern data platforms. Exposure to vector databases, embeddings, retrieval-augmented generation (RAG), and AI-ready data architectures. Retail, wholesale, supply chain, or large-scale transactional data experience. ...

intersection of DevOps, cloud infrastructure and applied AI owning the full lifecycle of Retrieval-Augmented Generation (RAG) and LLM-powered systems across modern cloud architecture. This role is about engineering, not research. You will architect and run the infrastructure that enables AI to perform … hardened, production-ready services while shaping a reusable AI platform capable of supporting multiple products. What You'll Be Doing Design and optimise scalable RAG pipelines and vector search systems Orchestrate multi-model AI services with a focus on latency, resilience and performance Productionise GenAI workflows and ensure they operate ...

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 ...

drive enterprise opportunities from qualification to close. Develop and maintain a strong perspective on how AI – including large language models, agentic architectures, and RAG-based solutions – can drive measurable business value across verticals and functions. Understand customer pain points in adopting and implementing AI solutions, and position Uniphore … trusted partner to address them. Deliver compelling technical presentations and demos showcasing GenAI, Agentic AI, RAG, and LLM-based capabilities tailored to each prospect’s context. Build and deliver custom demos and proof-of-concepts (POCs) to validate business value and technical feasibility. Conduct discovery sessions with key business ...

/GAI technologies and large language models (LLMs) Advanced AI Integration : Apply experience with retrieval-augmented generation (RAG), vector databases (e.g. Pinecone, Weaviate, FAISS), and enterprise search for AI-driven knowledge discovery Optimize AI Performance : Utilize practical experience designing structured prompts, fine-tuning models ...

Senior AI Engineer - AI Agents - High Profile AI Scale Up - (relocation to Dubai, UAE) Our client is building a next-generation digital platform, one that is deeply ethical, powered by thoughtful design, and built from first principles around AI. Their mission is to create a proactive, intelligent system … Build and maintain LLM agents and agentic workflows end-to-end Design retrieval-augmented generation (RAG) pipelines and grounding strategies Develop multi-step planning, tool use, and execution flows Implement approaches to reduce hallucinations and unsafe model behaviour Partner with product, backend ...

Iterate on model accuracy and retrieval performance using systematic evaluation frameworks (e.g., RAGAS, DeepEval). Build out our AI experiences — things like RAG pipelines , multimodal user experiences , and LLM-powered experiences that actually work in production. Embed Ignitus into the flow of work with low-complexity integrations Experiment … focus on building high-performance AI backends. Experience working with LLMs , vector databases , and retrieval-augmented generation (RAG). Strong understanding of embedding models, rerankers, and hybrid search. Hands-on experience deploying AI systems in production. Experience in dealing with IT audits with prospects. ...

agents for function classification, malware family identification, behavioral summarization, and IOC extraction. Create scalable pipelines for static and dynamic analysis, YARA generation Integrate AI models with binary analysis frameworks and disassemblers Fine-tune or evaluate models for reverse engineering and malware analysis tasks Build tooling for unpacking, emulation … cybersecurity or reverse engineering workflows Experience with AssemblyLine, ANGR, Capstone, QEMU, or Frida Familiarity with retrieval-augmented generation (RAG), embeddings, and vector search Knowledge of malware clustering and similarity analysis Experience building autonomous or semi-autonomous security agents Understanding of symbolic execution or program ...

About the Role We are seeking a Senior Full-Stack Engineer to join our team and help build the next generation of AI solutions. This is a unique opportunity to be a key technical contributor in a fast-paced, innovative environment where you'll wear many hats … trade-offs Experience with prompt engineering techniques — few-shot prompting, chain-of-thought, structured output, and instruction tuning Practical knowledge of RAG (Retrieval-Augmented Generation) architectures, including chunking strategies, vector embeddings, and similarity search Familiarity with LLM orchestration frameworks such as LangChain, LlamaIndex ...