12 of 12 Retrieval-Augmented Generation Jobs in the East of England

for a Data Scientist with strong NLP and Python skills, and hands-on experience with large language models (LLMs) and Retrieval-Augmented Generation (RAG). You'll be comfortable developing production-ready AI tools and collaborating across engineering, IT, and business functions. About the role: We're hiring a Data Scientist specialising in NLP … internal users across diverse teams - from design and manufacturing to systems engineering - developing tools powered by large language models (LLMs), Retrieval-Augmented Generation (RAG), and modern NLP techniques. From data preparation to algorithm design and deployment, you'll own the full data science lifecycle and help shape the future of AI capability across the … formed GenAI team, with plenty of opportunity to influence tooling, architecture, and strategy. Key Responsibilities: Design and deliver NLP and LLM-based algorithms tailored to internal user needs Apply RAG methods to provide accurate, context-rich AI responses using internal data Work with stakeholders across engineering, manufacturing, and systems teams to translate needs into AI solutions Clean, process, and manage More ❯

manage contract execution, search and retrieval, royalty calculations, and insight generation - increasingly supported by AI and retrieval-augmented generation (RAG) capabilities. We are looking for an Assistant Product Owner to support the evolution and delivery of this ecosystem, working alongside a lead Product Manager and cross-functional teams across Sales … Have Skills and Experience Exposure to internal business systems such as CRM, contract lifecycle management, or licensing platforms Understanding of AI-powered tools and document search (e.g. retrieval-augmented generation) Familiarity with Agile product delivery and tools such as Jira, Confluence, or Azure DevOps Basic knowledge of cloud services (AWS, Azure, GCP) Agile or More ❯

and Experience : Exposure to hardware design or verification processes (e.g., RTL, formal verification, simulation workflows). Familiarity with vector search, retrieval-augmented generation (RAG), or multi-agent orchestration. In Return: At Arm, you'll work on AI challenges with practical impact across a global organization. You'll join a collaborative team that values deep … technical insight, clean engineering and creative problem-solving. Your contributions will influence how thousands of engineers at Arm design and verify the next generation of computing platforms-smarter, faster, and at scale. Accommodations at Arm At Arm, we want to build extraordinary teams. If you need an adjustment or an accommodation during the recruitment process, please email . More ❯

Expertise Experience working with Large Language Models (LLMs) and understanding of trade-offs between performance, cost, and capability. Understanding of Retrieval-Augmented Generation (RAG), agent orchestration, prompt engineering, and tool calling. Familiarity with AI standards such as Model Context Protocol (MCP) and Agent2Agent (A2A). Strong knowledge or experience in working with various ML More ❯

Expertise Experience working with Large Language Models (LLMs) and understanding of trade-offs between performance, cost, and capability. Understanding of Retrieval-Augmented Generation (RAG), agent orchestration, prompt engineering, and tool calling. Familiarity with AI standards such as Model Context Protocol (MCP) and Agent2Agent (A2A). Strong knowledge or experience in working with various ML More ❯

cloud platforms and frameworks such as Azure AI/ML Studio, AWS Bedrock, GCP Vertex, Spark, TensorFlow, PyTorch, etc. Build and deploy production grade fine-tuned LLMs and complex RAG architectures. Create and manage the complex and robust prompts across the GenAI solutions. Communicate effectively with stakeholders and colleagues, using data visualisation, storytelling, and presentation skills. Ensure the ethical use More ❯

Strong understanding of AI technologies, including machine learning, natural language processing, or computer vision; or mathematical optimization. Experience with LLMs and Generative AI techniques such as fine tuning, pruning, RAG systems, transfer learning etc. Excellent command of Python, Docker and Git. Experience with GitHub/Gitlab workflows and functionalities. Solid understanding of agile development methodologies and best practices. Excellent leadership More ❯

high 2:1, preferably in a technical subject Proficiency in Python and its ecosystem Active engagement with the AI space and proactive exploration of new AI technologies (Agents, MCP, RAG, Chain-of-Continuous-Thought, etc. More ❯

in the fastest-moving fields of current AI research, in particular in how to integrate a broad range of structured and unstructured multimodal information into AI systems (e.g. with RAG techniques), and get to immediately apply your results in highly visible Amazon products. If you are deeply familiar with LLMs, natural language processing and machine learning, this may be the More ❯

learning, and contribute to a culture of innovation. AI & Cloud Expertise Familiarity with LLM evaluation techniques, output scoring, and validation frameworks. Understanding of key concepts such as prompt engineering, RAG, model orchestration, and hallucination detection. Experience in testing for accuracy, relevance, and consistency of AI model predictions/generations Defining Performance Metrics for AI services and testing for the same More ❯

SINDy) Working knowledge of cell biology. Experience with Python, C, R or related scientific computing languages. Preferred Qualifications - Experience working with causal representation learning Experience with RAG (retrieval-augmented generation) and GraphRAG a big plus Experience with building and deploying software on GitHub, PyPI, Anaconda Cloud, and Docker Hub, as well as use of More ❯

join our client in Pharma/Life Sciences. You'll be designing and building data infrastructure for an AI/ML solution and lead on prototyping and developing a RAG platform on AWS. You'll need: - Extensive AWS Data Engineering experience (S3, EC2, Glue, Lambada etc.) - NoSQL/MongoDB - Strong understanding of Data Modelling, ETL/ELT, DWH concepts - Docker …/EKS containerisation tooling - Experience building and maintaining pipelines for data workflows (CI/CD - RAG or LLM based data system experience If this sounds like you please reach out. More ❯