1 to 25 of 357 Retrieval-Augmented Generation Jobs in London

natural language understanding, and text generation. Ensure solutions are scalable, maintainable, and aligned with best practices in machine learning. Work on GenAI techniques like Prompt Engineering, RAG (Retrieval-Augmented Generation) and perform evaluation using frameworks to optimise LLM performance. Develop and implement machine learning workflows, focusing on the integration of GenAI with existing data … NLP, Generative AI and LLMs. Proficiency in Python and experience working with LLMs and NLP frameworks (e.g. Hugging Face, Spacy, Pytorch/Tensorflow etc). Experience with Prompt Engineering, RAG techniques and various evaluation methodologies for integrating GenAI with search/retrieval systems and measure the quality. Experience with LangChain/LlamaIndex, vector databases (e.g., FAISS), fine-tuning More ❯

push past the expected—with insight, integrity, and a passion for making things better. Role Overview We are looking for a Data Scientist with experience in RAG (Retrieval-Augmented Generation) , Agentic AI frameworks , and deep domain understanding of Finance/Savings platforms . This hybrid role (UK-based) is open to both Contract and … and business enablement . The ideal candidate thrives in complex environments and bridges the gap between data, automation, and user experience. Key Responsibilities AI-Driven Architecture: Design and implement RAG pipelines that enhance platform intelligence and content retrieval Agentic AI Integration: Develop and fine-tune autonomous agents for workflow automation, intelligent decisioning, and user engagement Domain Modelling: Leverage … BSc/MSc in Data Science, AI, Computer Science, or related quantitative field Experience: 5+ years in data science roles with platform/product experience 2+ years working on RAG implementations using tools like LangChain, LlamaIndex, or custom vector stores Experience building or deploying Agentic AI architectures (e.g., AutoGPT, CrewAI, OpenAgents) Strong grasp of CRM systems and finance/savings More ❯

source control and the Azure cloud. In this role you will You'll have the opportunity to work on a mixture of the following: Generative AI Design and develop RAG based applications. LLM fine-tuning, including preparation of training sets from internal data Agent based applications Evaluating use-case specific LLMs AI/ML NLP: Named Entity Recognition across a … looking for professionals with these required skills to achieve our goals: Bachelor's degree in computer science Extensive experience working in AI/ML Generative AI: Demonstratable experience of RAG, including chunking strategies, vectorising and indexing data, retrieval strategies and reranking, prompting strategies, function calling. Our current tech-stack is OpenAI, LangChain, Azure AI, Python, pg_vector, Sinequa. More ❯

role, you'll work closely with ML scientists, data engineers, and product teams to help bring innovative solutions-such as retrieval-augmented generation (RAG) systems, multi-agent architectures , and AI agent workflows -into production. As a Senior Machine Learning Engineer, you'll play a key role in developing and integrating cutting-edge AI solutions … a highly collaborative and fast-moving environment where your contributions will directly shape both the future of our platform and your own growth. Key Responsibilities Design, build, and deploy RAG systems , including multi-agent and AI agent-based architectures for production use cases. Contribute to model development processes including fine-tuning, parameter-efficient training (e.g., LoRA, PEFT), and distillation . … related technical discipline. Strong foundation in machine learning and data science fundamentals -including supervised/unsupervised learning, evaluation metrics, data preprocessing, and feature engineering. Proven experience building and deploying RAG systems and/or LLM-powered applications in production environments. Proficiency in Python and ML libraries such as PyTorch, Hugging Face Transformers , or TensorFlow. Experience with vector search tools (e.g. More ❯

APIs, or other LLM orchestration tools. A solid understanding of tokenisation, 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, summarisation tools, content generation workflows, or intelligent data extraction pipelines. Deep understanding of NLP fundamentals: text preprocessing More ❯

role, you’ll work closely with ML scientists, data engineers, and product teams to help bring innovative solutions—such as retrieval-augmented generation (RAG) systems, multi-agent architectures , and AI agent workflows —into production. As a Senior Machine Learning Engineer, you’ll play a key role in developing and integrating cutting-edge AI solutions … a highly collaborative and fast-moving environment where your contributions will directly shape both the future of our platform and your own growth. Key Responsibilities Design, build, and deploy RAG systems, including multi-agent and AI agent-based architectures for production use cases. Contribute to model development processes including fine-tuning, parameter-efficient training (e.g., LoRA, PEFT), and distillation. Build … related technical discipline. Strong foundation in machine learning and data science fundamentals—including supervised/unsupervised learning, evaluation metrics, data preprocessing, and feature engineering. Proven experience building and deploying RAG systems and/or LLM-powered applications in production environments. Proficiency in Python and ML libraries such as PyTorch, Hugging Face Transformers, or TensorFlow. Experience with vector search tools (e.g. More ❯

robust agentic workflows enabling AI agents to interact autonomously with data sources and external APIs using advanced prompt engineering and retrieval-augmented generation (RAG) Fine-tune and optimize pre-trained large language models and multi-modal models for targeted use cases, ensuring high performance and low latency in production. Implement distributed training and scalable More ❯

source control and the Azure cloud. In this role you will You'll have the opportunity to work on a mixture of the following: Generative AI Design and develop RAG based applications LLM fine-tuning, including preparation of training sets from internal data Agent-based applications Evaluating use-case specific LLMs AI/ML Engineering NLP: Named Entity Recognition across … in computer science Significant experience working in AI/ML and Python Strong Python programming skills with demonstrated expertise in building production-grade applications Generative AI: Demonstratable experience of RAG, including chunking strategies, vectorising and indexing data, retrieval strategies and reranking, prompting strategies, function calling. Our current tech-stack is OpenAI, LangChain, Azure AI, Python, pg_vector, Sinequa. More ❯

to date with the latest research and developments in the field of natural language processing (NLP) and machine learning. Work on GenAI techniques like Prompt Engineering , RAG (Retrieval-Augmented Generation) and perform evaluation using frameworks to optimise LLM performance. Develop and implement machine learning workflows, focusing on the integration of GenAI with existing data … infrastructure. Collaborate with the team to explore Generative AI use cases, including automated summarisation , natural language understanding , and text generation . Conduct experiments to evaluate model performance, identify areas for improvement, and implement enhancements. Perform continuous evaluations and improvements of models to handle large volumes of patent data. Work with stakeholders across teams to identify key areas for … and LLMs . Proficiency in Python and experience working with transformer based LLMs and NLP frameworks (e.g. Hugging Face, Spacy, Pytorch/Tensorflow etc ). Knowledge of Prompt Engineering , RAG techniques and various evaluation methodologies for integrating GenAI with search/retrieval systems and measure the quality . Experience working with cloud platforms like Azure , AWS , or GCP More ❯

used by global industry leaders. You'll play a pivotal role in transforming how our data are seamlessly integrated with AI systems, paving the way for the next generation of customer interactions. We are seeking a Data Scientist to join our Generative AI team. This role will focus on creating and maintaining AI-ready data, leveraging the deep … Large Language Models (LLMs) outputs, analysing quality, relevance, and accuracy, and supporting the implementation of LLM-as-a-judge frameworks. Retrieval-Augmented Generation (RAG) Contribution: Actively contribute to the implementation and optimization of RAG systems, including working with embedding models, vector databases, and, where applicable, knowledge graphs, to enhance data retrieval for … Expertise in prompt engineering, including prompt tuning, chaining, and optimization techniques. LLM Evaluation: Experience evaluating LLM outputs, including using LLM-as-a-judge methodologies to assess quality and alignment. RAG: Familiarity with vector databases. Cloud: Practical experience with Gemini/OpenAI models and cloud platforms such as AWS, Google Cloud, or Azure. Experience with Docker for containerization is a plus. More ❯

leveraging state-of-the-art models, LLMs and agentic workflows to explore new capabilities and product directions. Leverage machine learning techniques such as LLMs, RAG (retrieval augmented generation) and agentic orchestration frameworks to automate complex decision flows and generate insights from customer data. Mentor and guide a talented team of data scientists, fostering a … to production deployment. Comfort with ambiguity, and the ability to shape, test, and validate product ideas in fast-moving environments. Deep expertise in NLP and GenAI, including Transformers, LLMs, RAG architectures, fine-tuning, and/or agent-based workflows. Hands-on experience with recommendation systems or search-based applications. Strong collaboration skills, with a track record of working effectively across More ❯

Learning frameworks (TensorFlow, PyTorch, MLX) Popular classification and regression techniques Unsupervised learning & matrix factorisation algorithms Natural Language Processing (NLP) and document processing Generative AI (open and closed source) - LLM, RAG, Fine Tuning Performing model selection and evaluation Knowledge of cloud (AWS/Azure/GCP) Business skills Working directly with clients, users and data engineers Showcasing and presentation skills Evidence More ❯

techniques including test-driven development (TDD), Behaviour-driven development (BDD), integration testing and performance testing Some experience with AI tools (one of more of) Python, LLM(Large Language models), RAG, Langchain Set Yourself apart with: Bachelor's/Master's degree in Computer Science or related field Experienceof working on large scale, complex, and distributed applications in an Agile environment More ❯

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 ❯

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 ❯

and obstacles are addressed promptly. Ensure solutions are scalable, maintainable, and aligned with best practices in machine learning. Work on GenAI techniques like Prompt Engineering and RAG (Retrieval-Augmented Generation) to optimize LLM performance. Develop and implement machine learning workflows, integrating GenAI with existing data infrastructure. Perform continuous evaluations and improvements of models to … pipelines and leading a data science team. Proficiency in Python and experience with LLMs and NLP frameworks (e.g., Hugging Face, Spacy, Pytorch/Tensorflow). Knowledge of Prompt Engineering, RAG techniques, and evaluation methodologies for integrating GenAI with search/retrieval systems. Experience with LangChain/LlamaIndex, vector databases (e.g., FAISS), and fine-tuning models on domain-specific More ❯

to the highest standards. You'll be part of a multidisciplinary team focused on delivering enterprise-grade AI capabilities, including generative AI, agentic AI, LLMs and RAG (retrieval-augmented generation). The AI Engineer will optimise prompts to generative AI models across NiCE's Proactive AI Agent applications, working with several groups in the More ❯

tech and real-world impact, join us at this pivotal moment. The Role We’re looking for an inquisitive, hands-on Data Scientist Intern (Generative AI/LLM/RAG) for a 4-month internship programme . You’ll join our data and product teams to design, prototype, and evaluate solutions that leverage Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to improve customer experience, internal workflows, and decision-making. Think: building chatbots that tap our knowledge base, fine-tuning models on call-centre transcripts, or creating smart agents that surface the right maintenance engineer in seconds. You’ll see first-hand how generative AI moves the needle for a growing business. … You Will Prototype RAG pipelines —set up vector stores, devise retrieval strategies, and iterate on prompts Fine-tune or adapt foundation models (e.g., Llama-3, GPT-4o) to company-specific data Build evaluation harnesses to measure accuracy, latency, and hallucination rates Work with Python, LangChain/LlamaIndex, Hugging Face, and cloud LLM endpoints (OpenAI, Azure AI, etc.) Collaborate More ❯

tech and real-world impact, join us at this pivotal moment. The Role We’re looking for an inquisitive, hands-on Data Scientist Intern (Generative AI/LLM/RAG) for a 4-month internship programme . You’ll join our data and product teams to design, prototype, and evaluate solutions that leverage Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to improve customer experience, internal workflows, and decision-making. Think: building chatbots that tap our knowledge base, fine-tuning models on call-centre transcripts, or creating smart agents that surface the right maintenance engineer in seconds. You’ll see first-hand how generative AI moves the needle for a growing business. … You Will Prototype RAG pipelines —set up vector stores, devise retrieval strategies, and iterate on prompts Fine-tune or adapt foundation models (e.g., Llama-3, GPT-4o) to company-specific data Build evaluation harnesses to measure accuracy, latency, and hallucination rates Work with Python, LangChain/LlamaIndex, Hugging Face, and cloud LLM endpoints (OpenAI, Azure AI, etc.) Collaborate More ❯

tech and real-world impact, join us at this pivotal moment. The Role We’re looking for an inquisitive, hands-on Data Scientist Intern (Generative AI/LLM/RAG) for a 4-month internship programme . You’ll join our data and product teams to design, prototype, and evaluate solutions that leverage Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to improve customer experience, internal workflows, and decision-making. Think: building chatbots that tap our knowledge base, fine-tuning models on call-centre transcripts, or creating smart agents that surface the right maintenance engineer in seconds. You’ll see first-hand how generative AI moves the needle for a growing business. … You Will Prototype RAG pipelines —set up vector stores, devise retrieval strategies, and iterate on prompts Fine-tune or adapt foundation models (e.g., Llama-3, GPT-4o) to company-specific data Build evaluation harnesses to measure accuracy, latency, and hallucination rates Work with Python, LangChain/LlamaIndex, Hugging Face, and cloud LLM endpoints (OpenAI, Azure AI, etc.) Collaborate More ❯

to the highest standards. You’ll be part of a multidisciplinary team focused on delivering enterprise-grade AI capabilities, including generative AI, agentic AI, LLMs and RAG (retrieval-augmented generation). The AI Engineer will optimise prompts to generative AI models across NiCE's Proactive AI Agent applications, working with several groups in the More ❯

work closely with product and domain experts to identify compelling solutions at the intersection of user needs and technical feasibility. Our team is responsible for designing the next generation of risk and fraud investigation software. We own AI innovation for Thomson Reuters' core Risk and Fraud products, including CLEAR , CLEAR Adverse Media , and CLEAR Risk Inform . About … NLP/ML/Knowledge Graph/GenAI systems for commercial applications Practical experience with traditional and state-of-the-art NLP methods, Knowledge Graph algorithms, and GenAI (including RAG and agentic frameworks) Experience writing production code and ensuring well-managed software delivery Demonstrable experience translating complex problems into successful AI applications Outstanding communication, problem-solving, and analysis skills Collaborating More ❯

best practices in MLOps to streamline model deployment and monitoring across various environments. Provide authoritative guidance on state-of-the-art algorithms, repositories, and GenAI techniques like prompt engineering, RAG, AI agents in Generative AI space (LLMs) & share your experience with optimisation techniques such as quantization and pruning to minimise training & inference requirements for models. Design and implement effective LLM More ❯

or customer-facing roles, especially in the supply chain, demand forecasting, retail/cpg, manufacturing, marketing, financial services, or the public sector is a plus. Experience with LLM and RAG, GenAI, and agentic workflows is a plus. We care about our people’s lives, both inside and outside of causaLens. Beyond the core benefits like competitive remuneration, and a good More ❯

with modern AI tooling and ecosystems, including Hugging Face, Cursor, vector DBs, and productivity tools that accelerate GenAI development Expertise in GenAI and LLMs, with hands-on experience in RAG solutions and agentic frameworks; capable of leading end-to-end design and deployment of GenAI-driven systems Proven ability to manage projects with expert team members and to provide inspiring More ❯