101 to 125 of 335 Retrieval-Augmented Generation Jobs in the UK

growth and investment, specifically within our high-priority systematic trading and analytics systems. We are seeking a hands-on Engineering Lead to drive the development of our next-generation quantitative development platform serving the Equities Cash, Derivatives, and Prime businesses. This is a unique opportunity to make a significant impact as the platform enters its key growth phase. … Docker, Kubernetes) Proven success in enhancing developer experience that reduces friction in coding, building and deploying APIs and client libraries. Real-world application of generative AI prompt engineering and RAG pipelines. Full-stack HTML5 web development skills. Desired Skills: Understanding of Equities Cash, D1 & Deriv market mechanics and products via sell-side trading projects Familiarity with low-latency programming languages More ❯

Practical experience calling LLMs via APIs and dealing with varied responses Bonus skills Private equity or financial services experience Azure, Postgres, Streamlit or equivalents Fact extraction, Q&A and RAG on documents Comfort working in small teams with fast iteration cycles More ❯

Proficient in problem-solving and analytical reasoning. Exceptional communication and collaboration skills. Experience with ML frameworks such as TensorFlow, PyTorch, TensorRT, or ONNX. Experience with Large Language Models, including RAG and fine-tuning techniques. Familiarity with compute infrastructure necessary to support operating AI and ML technology. More ❯

we use it to power our analytics) OCR engines (we use AWS Textract, GDocAI, and we have used tesseractOCR in the past) Prompt Engineering Weaviate (we use it for RAG in LLM powered tasks and for hybrid searches) Kubernetes (we run Weaviate and other specific services on Kubernetes) CircleCI DataDog Auth0 (we use it, but we would rather not have 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 ❯

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 ❯

machine learning, data science or a related STEM field (degree or equivalent experience). Hands-on experience developing and deploying production-grade ML models, including advanced RAG (retrieval-augmented generation) systems. Deep expertise in Generative AI, LLMs, NLP, and Knowledge Graphs—with a track record of translating complex models into real-world business solutions. More ❯

machine learning, data science or a related STEM field (degree or equivalent experience). Hands-on experience developing and deploying production-grade ML models, including advanced RAG (retrieval-augmented generation) systems. Deep expertise in Generative AI, LLMs, NLP, and Knowledge Graphs-with a track record of translating complex models into real-world business solutions. 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 ❯

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 ❯

are: Experience delivering Large Language Model projects with customers, including LLM API integration, up-to-speed knowledge of foundation models, SFT (Supervised Fine-Tuning), prompt engineering, RAG (Retrieval-augmented generation) and/or measuring AI accuracy. Two years + experience in solutions architecture or integrating multiple applications/data streams, or ML development within More ❯

automate complex legal workflows and enhance user experiences. Advanced Technology Integration: Collaborate on projects that leverage emerging technologies - such as Retrieval-Augmented Generation (RAG) and Knowledge Graphs - to enhance our core product and explore new use cases. Cross-Functional Collaboration: Work closely with cross-functional teams to integrate advanced ML models and NLP solutions More ❯

and leveraging both structured and unstructured data sources. Experimenting with the integration and fine-tuning of models with Vector databases and embeddings to support semantic search, RAG (retrieval-augmented generation), and domain-specific applications. Working within a Data Mesh architecture, collaborating across domains to ensure scalable, interoperable data products; containerising solutions with Docker and More ❯

and leveraging both structured and unstructured data sources. Experimenting with the integration and fine-tuning of models with Vector databases and embeddings to support semantic search, RAG (retrieval-augmented generation), and domain-specific applications. Working within a Data Mesh architecture, collaborating across domains to ensure scalable, interoperable data products; containerising solutions with Docker and More ❯

and leveraging both structured and unstructured data sources. Experimenting with the integration and fine-tuning of models with Vector databases and embeddings to support semantic search, RAG (retrieval-augmented generation), and domain-specific applications. Working within a Data Mesh architecture, collaborating across domains to ensure scalable, interoperable data products; containerising solutions with Docker and More ❯

APIs (e.g., OpenAI, Claude, Hugging Face APIs) into internal tools and customer-facing applications. Design and implement workflows powered by AI, such as content summarization, support automation, documentation generation, or intelligent query handling. Develop prompt chains and prompt strategies for specific business functions (engineering assistance, product support, operational efficiency). Collaborate with cross-functional teams to identify and … with vector databases or semantic search tools. Experience building internal tooling or developer productivity enhancements using AI. Exposure to customer support AI tools or RAG (retrieval augmented generation) systems. Why Join Paydock? Work with a passionate, remote-first team that's reshaping payments. Opportunity to lead and define AI use cases at a scaling 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 ❯

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 ❯

across the business to identify and deliver high-value AI use cases Evaluate and experiment with emerging open-source models and tools (e.g., LLaMA, Mistral, DeepSeek) Design and maintain RAG pipelines and retrieval systems using unstructured data (e.g., PDFs, images, emails) Develop proof-of-concept projects with the Head of Data Science and scale successful solutions into production … Experience: 5+ years of experience with Python for data science and machine learning Strong background in NLP, machine learning, and hands-on work with open-source LLMs Experience building RAG pipelines and working with vector databases and embeddings If you are interested, please reach out for a private conversation- daniel.wexler@source-technology.com Unfortunately, this role does not offer sponsorship More ❯

with productionising and deploying. Strong Python, SQL and programming skills Hands on experience working within areas such as NLP, LLM, recommendation systems Information Retrieval stacks, GenAI (specifically RAG systems). Familiar with architecture and implementation of 1+ ML frameworks (PyTorch, scikit-learn). Experience working in the full model lifecycle (including experimentation, training, testing, monitoring, and deployment). 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 ❯

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 ❯

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 ❯

projects. You are proficient in programming languages such as Python, Java, TypeScript, and Go, and have experience with REST APIs and databases. You understand generative AI methodologies such as RAG, fine-tuning, agents, reasoning workflows, and more. You plan architecture and software with foresight to ensure automation, stability, and maintainability. Knowledge of cloud technologies (Azure or AWS), data science, Kubernetes More ❯

Quantum to share expertise, foster continuous learning, and rapidly re-use best practice ideas. Skills and experience we're looking for: Proficient in latest GenAI practiceslike prompt engineering and RAG systems, with awareness of emerging technologies such as Agentic AI. Skilled in integrating servicesvia Python-based pipelines, including package creation, and developing for user application interfaces. Knowledgeable in Kubernetes infrastructure More ❯