1 to 25 of 40 Retrieval-Augmented Generation Jobs in Central London

APIs, or other LLM orchestration tools. A solid understanding of tokenization, embedding models, vector databases (e.g., Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) pipelines. Experience designing and evaluating LLM-powered systems such as chatbots, summarization tools, content generation workflows, or intelligent data extraction pipelines. Deep understanding of NLP fundamentals: text preprocessing More ❯

architecture of LLMs. Foundational knowledge of diffusion models for image generation. Can display and present completed project/s using LLMs with a focus on any of the following: RAG, Agentic-RAG, fine-tuning Some experience or familiarity with deploying applications in the Cloud using services such as AWS or Azure. Proven track record in securing web/API applications. More ❯

and US investors . Our founders have delivered cutting-edge AI at world-class research labs and high-growth technology companies. Now, operating in stealth, we apply next-generation agentic AI to overhaul mission-critical enterprise workflows that still depend on error-prone, manual processes. Our vision is to bring these high-value operations into the modern era … event buses (Kafka, Pulsar). Wrangle large, heterogeneous data sets —model, transform, and index multi-modal, multi-terabyte enterprise datasets for advanced AI workloads Develop enterprise-level next generation AI systems with the support of our AI specialists Ship complete customer features - from architecture and code to CI/CD, infra-as-code (Terraform), rollout, and user training. … contract. Thrive in an early-stage, high-ownership environment—prototype today, deploy tomorrow, iterate next week. Bonus Points Experience deploying or consuming LLM-powered services (OpenAI, open-source models, RAG, vector stores) can be a bonus. However, we consider many great candidates without previous AI experience. What we're offering: Base salary from £115,000 - £135,000. .. plus meaningful 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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

Design and develop intelligent systems leveraging agentic AI concepts Integrate advanced machine learning models with reasoning, planning, and interaction modules Utilise prompt engineering, vector databases, and RAG (Retrieval-Augmented Generation) architectures Develop and deploy solutions using agent libraries such as Lang Chain, Lang Graph, and Autogen Apply computer vision and document processing techniques to … with cross-functional teams to implement scalable AI solutions Experience: Strong proficiency in Python programming Experience with large language models (LLMs) and prompt engineering Knowledge of vector databases and RAG architecture Hands-on experience with agentic libraries such as Lang Chain, Lang Graph, and Autogen Skilled in computer vision and document processing techniques Excellent problem-solving and system design skills More ❯

Anthropic, and Gemini. Implementing and 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 MongoDB Atlas Vector Search. Fine … and deep learning frameworks, particularly 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 MLOps principles and tools, including More ❯

nationwide rollout, while setting the gold-standard for safety, ethics, and governance in a regulated environment. As Director of AI & Research you will spearhead conversational AI, design next-generation AI-powered care models, and define rigorous guardrails to ensure the safety and confidentiality of all users. This is an opportunity to work on greenfield challenges, hands-on experimentation … the roadmap for conversational AI, generative LLMs, and predictive care models from ideation to production. Lead cutting-edge research - run rapid experiments, RLHF loops, fine-tuning, and retrieval-augmented generation to push the boundaries of clinical dialogue systems. Architect safe, scalable solutions - design reference & MLOps architectures on cloud with robust guard rails in place. More ❯

and maintaining long-lived systems. Bonus Points for: Familiarity with financial services or private equity environments. Experience with Azure, Postgres, Streamlit, or similar tools. Practical experience with document-based RAG, Q&A, or fact extraction tasks. Thriving in small teams with fast feedback and iteration loops. Please, apply to learn more. More ❯

solutions that transform digital interactions. The role will focus on projects to leverage state-of-the-art generative AI, retrieval-augmented generation (RAG), and reasoning frameworks to build intelligent and context-aware systems. We are seeking talented Machine Learning Engineers with full-stack software development experience to join our client's team and … varied duties will include: Search relevancy engineering. Conversational AI Development : Design, train, fine-tune, and deploy LLMs with reasoning capabilities. Retrieval-Augmented Generation (RAG): Implement, optimise, and scale RAG pipelines for effective information retrieval from structured and unstructured sources. Model Fine-Tuning & Training : Train domain-specific models using techniques like LoRA, QLoRA … Milvus, ChromaDB, or OpenSearch. Required skills & experience: 3-5+ years in machine learning and software development Proficient in Python, PyTorch or TensorFlow or Hugging Face Transformers Experience with RAG, LLM fine-tuning, and expertise in AWS and cloud-native AI deployments. Full-stack experience (React, TypeScript, Node.js) and API development. Familiarity with vector search and multi-agent orchestration Apply More ❯

Skills & Expertise Experience deploying and managing applications using Azure and Docker. Familiarity with frameworks such as LangChain and expertise in Retrieval-Augmented Generation (RAG) models for AI-driven applications. Proficiency with pandas for data manipulation. Full Stack Developer - What's in it for you? Salary Reviews: Twice a year to recognise your contributions. Generous More ❯

decisions. Ultimately, a solution that will change financial management for SMEs forever and unlock financial literacy at scale by building the most trusted financial brain for the next generation of businesses. If you're excited to help invent a new category of AI, build one of the biggest new categories in fintech and change how millions of companies … Demonstrated experience implementing AI agents Expertise in memory systems and context management for AI agents Strong coding skills in Python and experience with Typescript Experience setting up and developing RAG flows Familiarity with vector databases Advanced prompt engineering capabilities Experience with LLM orchestration frameworks (LangChain, LangGraph, LlamaIndex, etc.) Track record of shipping reliable AI systems to production Valuable Additional Skills More ❯

consulting, cloud application architecture, or low-code/no-code app delivery. Copilot Studio Expertise : Deep understanding of native Copilot Studio capabilities and integrations with Azure Open AI for RAG implementations. Power Platform Skills : Hands-on experience with Microsoft Power Platform, including Copilot Studio, Power Apps, Power Automate, Power Pages, AI Builder, and Dataverse. Azure AI Skills : Proficient in using … platforms. Programming Proficiency : Skilled in one or more programming languages such as C#, Python, or JavaScript. AI Knowledge : Familiarity with AI concepts and frameworks, including Large Language Models(LLM), RAG, Prompt Engineering, Azure Cognitive Services and AI Builder. DevOps Knowledge Certifications : Relevant certifications in Microsoft Power Platform and/or Azure/AWS cloud. About Capgemini Capgemini is a global More ❯

Up to £110k + equity 💻 Python, PostgreSQL, LLMs, RAG, Semantic Search, Knowledge Representation, Fine-tuning 🏠 Central London | 4-days on-site 🚀 Seed-funded. Aiming for Series A in 2025 We’re working with an exciting AI Ed-tech start-up in London that’s transforming the educational experience. Fresh from securing Seed funding and backed by one of the world More ❯

In addition to this you will have experience developing LLM applications using some of the more recent LLMs such as GPT, Llama, Claude, Gemini, Qwen and Mistral models, developed RAG pipelines and architecture, orchestrated workflows using LangChain/LangGraph/LlamaIndex etc. This role is ideal for someone with.... 2 years of experience developing and deploying BERT based NLP models More ❯

to continuously improve usability and performance What You Bring Experience deploying LLM and generative AI systems into production Proficiency with Python, containerisation (Docker), and cloud (Azure preferred) Familiarity with RAG, prompt engineering, chaining, and performance evaluation A builder mindset - pragmatic, curious, and user-first Bonus if you’ve worked with: Multimodal data (e.g. voice + text) Healthcare, social care, or More ❯

re looking for a software engineer who can design, build, and deploy AI systems end-to-end — especially those involving retrieval-augmented generation (RAG) , embeddings, and LLM orchestration. This is a hands-on role with real impact and ownership. 🔍 What You'll Work On Architect and implement RAG pipelines that combine vector search, custom … embedding models, vector DBs (e.g., FAISS, Weaviate, Qdrant), and retrieval logic Strong Python engineering skills — you write clean, production-ready code with tests Experience building and evaluating RAG pipelines in a real-world setting Familiarity with LLM evaluation techniques — you don’t deploy until you’ve tested against real metrics Solid understanding of modern cloud infrastructure (e.g., Docker More ❯

Mid-Level AI Engineer | AI-Driven Digital Transformation Salary: Up to £75,000 plus benefits Technology: Python, Microservices, CI/CD, AI/ML, LLM or RAG Location: Hybrid/London (onsite twice a week) Multiple Vacancies An innovative, fast-growing company transforming digital client experiences through intelligent technology is seeking a number of Mid-Level AI Engineers to join … Python programming skills with backend development experience Interest or hands-on exposure to AI/ML concepts, particularly LLMs and Retrieval-Augmented Generation (RAG) Experience working with AWS or similar cloud platforms Understanding of microservices architecture and distributed systems Familiarity with container tools such as Docker and Kubernetes Experience with CI/CD tools More ❯

object detection (e.g. MobileNet, YOLO). Either way, we’re looking for someone who can help our app understand what the visitor is looking at – reliably and at scale. RAG Systems, Data Pipelines & Internal Agents: You'll design the data pipelines that power our AI features, including retrieval-augmented generation (RAG), internal LLM-based More ❯

time using image embeddings, similarity search (e.g. CLIP, vector search), and traditional CV approaches (e.g. YOLO, MobileNet). 🔹 LLM & RAG Systems: Design and implement pipelines that support retrieval-augmented generation, internal AI tools, and scalable content delivery. Experience with vector databases, agent frameworks, or data workflows is highly relevant. 🔹 Deployment & MLOps: Own model deployment More ❯