1 to 25 of 90 Retrieval-Augmented Generation Jobs in London

looking for a highly skilled Lead AI Solutions Engineer to design, build, and optimize a Retrieval-Augmented Generation (RAG) system that underpins our self-serve analytics data applications. In this pivotal position, you will develop a scalable RAG platform, integrate multiple data sources, and … create intuitive data interactions to empower teams across the organization. As part of our Group's RAG Initiative, you will collaborate with a cross-functional team to implement self-service AI-driven solutions - ranging from NLP Data Analysis and Data Discovery to other analytics assistants we will be deploying to … s about reshaping our operational DNA to drive innovation, efficiency, and exceptional customer experiences in the new era of analytics and automation. Key Responsibilities: RAG System Development Enhance, build new assistants and maintain existing scalable and modular RAG architectures for data retrieval and generation. Develop APIs and microservices More ❯

to redefine primary care while helping people live happier, healthier, and longer. Education Masters Degree About the Role You will design, build, and optimize RAG pipelines that combine large language models (LLMs) with domain-specific retrieval systems, enabling natural language understanding and reasoning over patient data, clinical guidelines … work will directly impact how patients and clinicians interact with our platform, enabling safe, accurate, and context-aware content surfacing. Responsibilities Design and implement RAG architectures using open-source and potentially proprietary LLMs (e.g., LLaMA, Mistral, OpenAI, Anthropic). Build and maintain retrieval pipelines over structured and unstructured … notes, device logs, clinical documentation). Develop indexing strategies using vector databases (e.g., FAISS, Weaviate, Pinecone) and embedding models (e.g., BioBERT, ClinicalBERT). Integrate RAG outputs into user-facing applications, ensuring responses are grounded, reliable, and privacy-compliant. Work closely with product, clinical, and data science teams to fine-tune More ❯

to redefine primary care while helping people live happier, healthier, and longer. Education Masters Degree About the Role You will design, build, and optimize RAG pipelines that combine large language models (LLMs) with domain-specific retrieval systems, enabling natural language understanding and reasoning over patient data, clinical guidelines … work will directly impact how patients and clinicians interact with our platform, enabling safe, accurate, and context-aware content surfacing. Responsibilities Design and implement RAG architectures using open-source and potentially proprietary LLMs (e.g., LLaMA, Mistral, OpenAI, Anthropic). Build and maintain retrieval pipelines over structured and unstructured … notes, device logs, clinical documentation). Develop indexing strategies using vector databases (e.g., FAISS, Weaviate, Pinecone) and embedding models (e.g., BioBERT, ClinicalBERT). Integrate RAG outputs into user-facing applications, ensuring responses are grounded, reliable, and privacy-compliant. Work closely with product, clinical, and data science teams to fine-tune More ❯

with experience in deploying and leading complex AI/ML workloads. Implemented production applications using Retrieval-augmented generation (RAG) concepts and managed large Knowledge Bases (KBs) with embeddings, chunking and other optimization techniques within VectorDBs. Expert programming skills in languages such as Python, Java More ❯

AI initiatives span categorization, summarization, data analysis, automated content generation, and rich media analysis - leveraging both existing AI/ML tools (LLMs, RAG, embeddings, agentic workflows) and custom solutions. You'll collaborate across teams to build practical, scalable AI solutions - where precision matters. With a "you build it … Select, train, and fine-tune AI models based on need, leveraging appropriate techniques (e.g. LLMs, Retrieval-Augmented Generation (RAG), Vector Databases, Embeddings, Multi-modal AI, Agentic Workflows) (Model Development). Integrate and deploy scalable AI based applications, backend services and APIs (Deployment). Develop More ❯

Lead AI Solutions Engineer to join their team! You will be designing, building and optimising Retrieval-Augmented Generation (RAG) systems which forms the basis of their data applications. You will be responsible for developing an RAG platform at scale, integrating numerous data sources and … limited to: Collaborate with cross-functional teams including AI, ML & Analytics to deliver impactful self-service AI-driven data solutions. Design, build and optimise RAG systems at scale which multiple teams will utilise. Develop APIs & dbt to integrate RAG capabilities with existing data sources. Build robust and scalable data pipelines … AI Solutions Engineer will have the following: 5+ years of experience in AI/ML integration, data engineering or backend development. Solid experience with RAG, NLP techniques and LLM-based architectures. Strong knowledge of APIs & dbt, as well as proficiency in SQL & data warehousing concepts. Experience with Terraform & Kubernetes for More ❯

Lead AI Solutions Engineer to join their team! You will be designing, building and optimising Retrieval-Augmented Generation (RAG) systems which forms the basis of their data applications. You will be responsible for developing an RAG platform at scale, integrating numerous data sources and … limited to: Collaborate with cross-functional teams including AI, ML & Analytics to deliver impactful self-service AI-driven data solutions. Design, build and optimise RAG systems at scale which multiple teams will utilise. Develop APIs & dbt to integrate RAG capabilities with existing data sources. Build robust and scalable data pipelines … AI Solutions Engineer will have the following: 5+ years of experience in AI/ML integration, data engineering or backend development. Solid experience with RAG, NLP techniques and LLM-based architectures. Strong knowledge of APIs & dbt, as well as proficiency in SQL & data warehousing concepts. Experience with Terraform & Kubernetes for More ❯

NLP), Generative AI, Multimodal Large Language Model (MLLM), Natural Language Understanding (NLU), Machine Learning (ML), Retrieval-Augmented Generation (RAG), Computer Vision, Responsible AI, LLM Agents, Evaluation, and Model Adaptation. Key job responsibilities As an Applied Scientist on our team, you will be responsible for … test experiments, optimize and deploy your models into production, working closely with software engineers. Establish automated processes for large-scale data analysis and generation, machine-learning model development, model validation and serving. Communicate results and insights to both technical and non-technical audiences, including through presentations and written More ❯

experiment, and ship new features. Explore and implement advanced concepts such as multi-agent systems, retrieval-augmented generation (RAG), and agentic architectures. Champion quality and reuse across the product and the codebase. Work across the business to ensure the features you develop have a More ❯

LLM technologies and apply them to production environments, including third-party and open-source LLMs, Retrieval-Augmented Generation (RAG), model fine-tuning, structured output, and external connectors. Collaborate with instructional designers to refine and optimize prompt engineering for enhanced learning outcomes. Develop and maintain More ❯

systems for high-speed, accurate search functionality. • Explore and implement advanced techniques, such as Retrieval-Augmented Generation (RAG), to enhance AI capabilities. • Conduct experiments to measure and improve the performance of NLP models. • Build custom language models tailored to specific industry requirements. What More ❯

the forefront of tech, working with advanced AI models, including large language models (LLMs) and Retrieval-Augmented Generation (RAG) techniques, gaining hands-on experience with the latest innovations. High-impact role: Your contributions will directly shape BRYTER's growth and success. Collaborative and innovative More ❯

implementation of AI solutions across Morningstar's business units. Requirements: Minimum 3 years of hands-on experience implementing AI/machine learning solutions, including RAG , NLP , AI Agents , and transformer models, in commercial applications. Advanced degree (Master's/PhD preferred) in a quantitative, AI or computational field such as More ❯

Computer Science, Mathematics or a similar quantitative discipline Understanding of NLP algorithms and techniques and/or experience with Large Language Models (fine tuning, RAG, agents) Are proficient with Python, including open-source data libraries (e.g Pandas, Numpy, Scikit learn etc.) Have experience productionising machine learning models Are an expert More ❯

and optimising hyperparameters for improved performance Skilled in building LLM-powered applications for real-time decision-making and automation across various domains Familiarity with RAG and agentic workflows Solid understanding of machine learning algorithms, deep learning architectures, and their applications Strong problem-solving skills and the ability to translate complex More ❯

Natural Language Processing and Generative AI to join our AI Experiences team. Our teams are working on exciting initiatives such as: Building and deploying RAG systems, curating data for model training and evaluation, building evaluation systems that facilitate rapid iteration, and understanding how users interact with our systems to identify More ❯

Natural Language Processing and Generative AI to join our AI Experiences team. Our teams are working on exciting initiatives such as: Building and deploying RAG systems, curating data for model training and evaluation, building evaluation systems that facilitate rapid iteration, and understanding how users interact with our systems to identify More ❯

independently and collaboratively. Preferred Skills: Experience building scalable applications with LLMs, using frameworks such as LangChain, LlamaIndex, Hugging Face, etc. Depth of knowledge with RAG implementation and improvements. More ❯

AI-powered solutions. Maintain productive relationships with clinical teams, data scientists, and infrastructure teams to ensure successful deployment of NLP products - including LLM systems, RAG pipelines, and autonomous clinical agents. Provide detailed performance metrics and analysis of NLP systems, including model accuracy, latency, and clinical impact metrics to the Head More ❯

Ideally AWS) Strong Python & SQL experience Experience in advertising, marketing or YouTube/social media video (Highly Desirable) Experience or understanding of LLMS, LangChain, & RAG (Beneficial) Sound of interest? Apply today for immediate consideration! Please note: All candidates must be UK based and have the full right to work in More ❯

Ideally AWS) Strong Python & SQL experience Experience in advertising, marketing or YouTube/social media video (Highly Desirable) Experience or understanding of LLMS, LangChain, & RAG (Beneficial) Sound of interest? Apply today for immediate consideration! Please note: All candidates must be UK based and have the full right to work in More ❯

Strong understanding of machine learning algorithms, NLP, and LLMs with demonstrated business application expertise Experience developing AI-powered automation systems, intelligent assistants/copilots, RAG systems, voice interfaces, and computer vision applications (image and video processing) for enterprise environments Knowledge of advanced AI agent frameworks and architectures such as ReAct More ❯

Alexa+. We work on recent advances in LLM science including automated prompt optimisation, supervised fine tuning, reinforcement learning, model distillation, retrieval augmented generation, model & prompt interpretability and LLM driven data generation. The techniques are used to tune a foundational model to align with Alexa More ❯

needed Our client is an international company that requires a senior Data Scientist with experience in Azure Databricks, Knowledge Graph, Neo4J Graph Database, and RAG pipelines for LLM to join the team. Job Description: Responsibilities: Develop and implement data models and algorithms to solve complex business problems. Utilize Databricks to More ❯

Instruction Tuning, Reinforcement Learning Alignment, Distributed Training, Efficient ML techniques) Post-training techniques for planning & reasoning (e.g. LLMs & Knowledge Graphs, Advanced Prompting, CoT pipelines, RAG, Tool use, etc.) Data-centric Machine Learning (Synthetic Data, Curriculum Learning, learned data-mixtures) Evaluation (Benchmarks, Human-in-the-loop, red teaming/Adversarial Testing More ❯