26 to 50 of 168 Retrieval-Augmented Generation Jobs in London

varied use cases. Build agentic workflows and reasoning pipelines using frameworks such as LangChain, LangGraph, CrewAI, Autogen, and LangFlow. Implement retrieval-augmented generation (RAG) pipelines using vector databases like Pinecone, FAISS, Chroma, or PostgreSQL. Fine-tune prompts to optimise performance, reliability, and alignment. Design and implement memory modules for short-term and long-term … cloud AI tools, observability platforms, and performance optimisation. This is an opportunity to work at the forefront of AI innovation, where your work will directly shape how next-generation systems interact, reason, and assist. More ❯

varied use cases. Build agentic workflows and reasoning pipelines using frameworks such as LangChain, LangGraph, CrewAI, Autogen, and LangFlow. Implement retrieval-augmented generation (RAG) pipelines using vector databases like Pinecone, FAISS, Chroma, or PostgreSQL. Fine-tune prompts to optimise performance, reliability, and alignment. Design and implement memory modules for short-term and long-term … cloud AI tools, observability platforms, and performance optimisation. This is an opportunity to work at the forefront of AI innovation, where your work will directly shape how next-generation systems interact, reason, and assist. More ❯

varied use cases. Build agentic workflows and reasoning pipelines using frameworks such as LangChain, LangGraph, CrewAI, Autogen, and LangFlow. Implement retrieval-augmented generation (RAG) pipelines using vector databases like Pinecone, FAISS, Chroma, or PostgreSQL. Fine-tune prompts to optimise performance, reliability, and alignment. Design and implement memory modules for short-term and long-term … cloud AI tools, observability platforms, and performance optimisation. This is an opportunity to work at the forefront of AI innovation, where your work will directly shape how next-generation systems interact, reason, and assist. More ❯

lead the development of advanced AI agents, leveraging LLMs, reinforcement learning, vector databases, and autonomous systems . As a key AI strategy leader , you'll define the next-generation consumer experiences , enabling real-time merchant insights , AI-powered Ops automation , and cutting-edge chatbot capabilities . You will collaborate closely with executive leadership, key industry stakeholders, and regulatory … capable of autonomous learning, decision-making, and task execution. LLM Fine-Tuning & Enhancement: Adapt foundational models like GPT, Llama, integrating retrieval-augmented generation (RAG), personalization, and continuous improvement loops. Multi-Agent Systems: Implement collaborative AI systems that solve complex challenges in payments and customer interactions. Long-Term AI Memory & Personalization: Develop solutions to enhance … in Computer Science, AI, Machine Learning, or related field. Experience: 7+ years in AI/ML development, with expertise in Python, TensorFlow, and PyTorch. Core AI Knowledge: LLM Optimization (RAG, fine-tuning, prompt engineering) Vector databases for AI retrieval Multi-agent systems and reinforcement learning Cloud AI deployment (AWS, Azure), inference optimization A snippet of what you'll More ❯

professional development! Requirements: Strong skills in Python scripting skills Strong understanding of LLMs Experience delivering Gen-AI projects Experience with Retrieval-Augmented Generation (RAG) Experience with Microsoft data technologies Experience with Cloud platforms – ideally Azure Strong communication, stakeholder management and problem-solving skills Benefits: Salary of up to around £60,000 depending upon experience More ❯

healthcare and cutting-edge LLM technology, shipping fast and solving meaningful problems every day. What You’ll Own Architect and develop backend microservices (Python/FastAPI) that power our RAG pipelines and analytics Build scalable infrastructure for retrieval and vector search (PGVector, Pinecone, Weaviate) Design evaluation frameworks to improve search accuracy and reduce hallucinations Deploy and manage services … LlamaIndex What We’re Looking For 5+ years building production-grade backend systems (preferably in Python) Strong background in search, recommender systems, or ML infrastructure at scale Experience with RAG architectures, embeddings, and vector search Confidence working across GCP (or AWS/Azure) and infrastructure-as-code Familiarity with observability, performance tuning, and secure data practices A growth mindset, startup More ❯

healthcare and cutting-edge LLM technology, shipping fast and solving meaningful problems every day. What You’ll Own Architect and develop backend microservices (Python/FastAPI) that power our RAG pipelines and analytics Build scalable infrastructure for retrieval and vector search (PGVector, Pinecone, Weaviate) Design evaluation frameworks to improve search accuracy and reduce hallucinations Deploy and manage services … LlamaIndex What We’re Looking For 5+ years building production-grade backend systems (preferably in Python) Strong background in search, recommender systems, or ML infrastructure at scale Experience with RAG architectures, embeddings, and vector search Confidence working across GCP (or AWS/Azure) and infrastructure-as-code Familiarity with observability, performance tuning, and secure data practices A growth mindset, startup More ❯

healthcare and cutting-edge LLM technology, shipping fast and solving meaningful problems every day. What You’ll Own Architect and develop backend microservices (Python/FastAPI) that power our RAG pipelines and analytics Build scalable infrastructure for retrieval and vector search (PGVector, Pinecone, Weaviate) Design evaluation frameworks to improve search accuracy and reduce hallucinations Deploy and manage services … LlamaIndex What We’re Looking For 5+ years building production-grade backend systems (preferably in Python) Strong background in search, recommender systems, or ML infrastructure at scale Experience with RAG architectures, embeddings, and vector search Confidence working across GCP (or AWS/Azure) and infrastructure-as-code Familiarity with observability, performance tuning, and secure data practices A growth mindset, startup More ❯

LLM pipelines, ensuring versioning, monitoring, and adherence to best practices. Drive integration of external knowledge bases and retrieval systems to augment LLM capabilities. Research and Development: Develop RAG architectures, organize complex domain-specific data (e.g., vector databases, knowledge graphs), and implement knowledge extraction. Benchmark and tune state-of-the-art LLMs for performance and cost efficiency. Incorporate trends … least 2 years in a senior or lead role. Proven expertise with modern LLMs in production. Leadership skills: Proven leadership in agile environments, strong communication, mentoring abilities. LLM and RAG expertise: Building RAG architectures, integrating vector and graph databases. Transformer and LLM architectures: Experience with GPT-4, Claude, Gemini, Llama, Falcon, Mistral. Model performance and optimization: Fine-tuning and optimizing More ❯

tier third-party vendors, platforms, and internal tech teams. Enable Cross-Functional Success: Work closely with IT, Big Data, Security, Digital, and Business Units. Innovate with AI: Leverage LLMs, RAG, MLOps, and cloud-native tools to build scalable, secure solutions. Ensure Governance: Align with enterprise standards, responsible AI practices, and compliance frameworks. Deliver Measurable Impact: Define success metrics and ensure … is realized at every stage. Key Skills and Experience: Proven experience architecting AI/ML or GenAI solutions in complex, enterprise environments. Hands-on expertise with LLMs, NLP, MLOps, RAG pipelines, APIs, and real-time data systems. Strong track record in networks, telecom, or customer experience domains (preferred). Proficiency in cloud platforms like GCP, AWS, or Azure; plus tools More ❯

tier third-party vendors, platforms, and internal tech teams. Enable Cross-Functional Success: Work closely with IT, Big Data, Security, Digital, and Business Units. Innovate with AI: Leverage LLMs, RAG, MLOps, and cloud-native tools to build scalable, secure solutions. Ensure Governance: Align with enterprise standards, responsible AI practices, and compliance frameworks. Deliver Measurable Impact: Define success metrics and ensure … is realized at every stage. Key Skills and Experience: Proven experience architecting AI/ML or GenAI solutions in complex, enterprise environments. Hands-on expertise with LLMs, NLP, MLOps, RAG pipelines, APIs, and real-time data systems. Strong track record in networks, telecom, or customer experience domains (preferred). Proficiency in cloud platforms like GCP, AWS, or Azure; plus tools More ❯

internal AI Centre of Excellence. Act as the primary AI representative in leadership and product discussions. Deliver generative AI solutions - Design and deploy GenAI applications (e.g. virtual assistants, copilots, RAG systems) Set up scalable LLMOps pipelines: model evaluation, versioning, and governance. Monitor developments in the GenAI landscape and evaluate adoption paths. Support machine learning projects - Collaborate with ML Engineers on … analysis. Help shape the future team structure, tool stack, and hiring strategy. About You Must-Haves Experience delivering production-ready GenAI solutions Solid grasp of LLMs, prompt engineering, and RAG workflows Familiarity with MLOps practices and deployment of classical ML models Competence in statistical modelling and A/B testing frameworks Proficiency in Python and at least one other programming More ❯

of experience Experience in Knowledge Graphs or Large Document Search Experience with traditional ML models and feature engineering. Strong Experience with fine tuning, modelling and deploying LLMs - experience with RAG, IR, NER etc would also be very beneficial Strong programming skills (e.g., Python) and experience with modern ML frameworks (e.g., PyTorch, TensorFlow, LangChain). Collaborating with other Researchers, Product, Engineering More ❯

of experience Experience in Knowledge Graphs or Large Document Search Experience with traditional ML models and feature engineering. Strong Experience with fine tuning, modelling and deploying LLMs - experience with RAG, IR, NER etc would also be very beneficial Strong programming skills (e.g., Python) and experience with modern ML frameworks (e.g., PyTorch, TensorFlow, LangChain). Collaborating with other Researchers, Product, Engineering More ❯

with product, data, and engineering teams Mentor peers and bring cutting-edge ML to life in production What You Bring: Strong Python + ML/LLM stack (Transformers, LangChain, RAG, vector DBs) Experience with NLP, embeddings, and cloud tools (AWS preferred) Ability to solve complex problems and ship real impact Passion for AI, autonomy, and collaborative innovation Bonus: 3D, GIS More ❯

Saatchi brand. If this interests you, please keep reading below for more information. Responsibilities • Design, develop and maintain machine learning, deep learning, and AI prototype tools (including LLM and RAG products)• Collaborate with Audience Strategists to solve business problems using data science• Lead and mentor junior data scientists and analysts• Work with Publicis Groupe technology teams to scale prototypes into More ❯

and n8n. Apply Large Language Models (LLMs) to real-world challenges like email triage, document parsing, and data enrichment. Implement retrieval-augmented generation (RAG) to power internal AI assistants with trusted business knowledge. Rapidly prototype and iterate on Proofs of Concept to test ideas and gather user feedback early. Use AI agents to automate … Experience using Python, OpenAI, LangChain, and vector databases. Familiarity with automation platforms like n8n, Zapier, or similar. Solid understanding of retrieval-augmented generation (RAG) methods. Strong problem-solving skills and the ability to design practical AI solutions with business outcomes in mind. Excellent communication skills – able to work cross-functionally and explain technical concepts More ❯

clinicians to make faster, evidence-based decisions at the bedside. We’re developing a cutting-edge platform that combines LLMs , retrieval-augmented generation (RAG) , and vector search infrastructure to deliver real-time clinical insights. As the Founding Backend Engineer , you’ll play a critical role in shaping our backend systems, architecture, and engineering culture … from the ground up. What You’ll Do Build scalable backend microservices in Python (FastAPI) to support RAG workflows and user queries Develop and optimise vector search pipelines using tools like PGVector, Pinecone, or Weaviate Design embedding orchestration and hybrid retrieval mechanisms Implement evaluation frameworks (BLEU, ROUGE, hallucination checks) to monitor answer quality Deploy production systems on GCP More ❯

clinicians to make faster, evidence-based decisions at the bedside. We’re developing a cutting-edge platform that combines LLMs , retrieval-augmented generation (RAG) , and vector search infrastructure to deliver real-time clinical insights. As the Founding Backend Engineer , you’ll play a critical role in shaping our backend systems, architecture, and engineering culture … from the ground up. What You’ll Do Build scalable backend microservices in Python (FastAPI) to support RAG workflows and user queries Develop and optimise vector search pipelines using tools like PGVector, Pinecone, or Weaviate Design embedding orchestration and hybrid retrieval mechanisms Implement evaluation frameworks (BLEU, ROUGE, hallucination checks) to monitor answer quality Deploy production systems on GCP More ❯

clinicians to make faster, evidence-based decisions at the bedside. We’re developing a cutting-edge platform that combines LLMs , retrieval-augmented generation (RAG) , and vector search infrastructure to deliver real-time clinical insights. As the Founding Backend Engineer , you’ll play a critical role in shaping our backend systems, architecture, and engineering culture … from the ground up. What You’ll Do Build scalable backend microservices in Python (FastAPI) to support RAG workflows and user queries Develop and optimise vector search pipelines using tools like PGVector, Pinecone, or Weaviate Design embedding orchestration and hybrid retrieval mechanisms Implement evaluation frameworks (BLEU, ROUGE, hallucination checks) to monitor answer quality Deploy production systems on GCP More ❯

with a world-class team of engineers, researchers, and product thinkers to bring AI features to life. Design and maintain retrieval-augmented generation (RAG) and agentic systems that power real-world use cases. Stay ahead of the curve by experimenting with the latest in AI research and tooling—and bring those ideas to production. … passion for solving hard problems with elegant code. Excellent communication skills—you can explain complex ideas clearly to both technical and non-technical audiences. 💡 Bonus Points For Experience with RAG pipelines and improving retrieval performance. Contributions to open-source AI projects. A portfolio of AI-powered products or prototypes you’ve helped bring to life. 🚀 Why You’ll More ❯

with a world-class team of engineers, researchers, and product thinkers to bring AI features to life. Design and maintain retrieval-augmented generation (RAG) and agentic systems that power real-world use cases. Stay ahead of the curve by experimenting with the latest in AI research and tooling—and bring those ideas to production. … passion for solving hard problems with elegant code. Excellent communication skills—you can explain complex ideas clearly to both technical and non-technical audiences. 💡 Bonus Points For Experience with RAG pipelines and improving retrieval performance. Contributions to open-source AI projects. A portfolio of AI-powered products or prototypes you’ve helped bring to life. 🚀 Why You’ll More ❯

infrastructure Working knowledge of Kubernetes, security best practices, and cloud platforms (AWS, GCP, or Azure) Desirable: Experience with prompt engineering, Retrieval-Augmented Generation (RAG), and graph databases Familiarity with multi-agent LLM systems and agentic platforms (e.g., AutoGen, CrewAI), and experience deploying LLM-based applications Experience with tools such as LangChain, LangSmith, or Chainlit More ❯

infrastructure Working knowledge of Kubernetes, security best practices, and cloud platforms (AWS, GCP, or Azure) Desirable: Experience with prompt engineering, Retrieval-Augmented Generation (RAG), and graph databases Familiarity with multi-agent LLM systems and agentic platforms (e.g., AutoGen, CrewAI), and experience deploying LLM-based applications Experience with tools such as LangChain, LangSmith, or Chainlit More ❯

integrated into enterprise applications to enhance user experience, decision-making, and automation. Exposure to modern AI application patterns such as: Retrieval-Augmented Generation (RAG) for augmenting LLMs with domain-specific knowledge. Prompt engineering and fine-tuning for tailoring model behavior to business-specific contexts. Use of embedding stores and vector databases (e.g., Pinecone, Redis More ❯