1 to 25 of 153 Permanent Retrieval-Augmented Generation Jobs in London

classification, object detection, and segmentation production models. Additionally, this role will involve pushing boundaries by building innovative GenAI applications, particularly focusing on API usage and Retrieval Augmented Generation (RAG). You will be joining a team whose mission is to streamline vehicle profiling and transform the online vehicle selling and buying experience for all … of machine learning principles, deep learning techniques and GenAI concepts such as prompt engineering, chain-of-thought reasoning, prompt chaining, Retrieval-Augmented Generation (RAG), custom-built agents. Familiarity with LLM and agentic frameworks like LangChain, PydanticAI, or similar. Proficiency in ML-Ops practices and tools; strong understanding of DevOps and CI/CD. Experience … preferred), GCP, and deploying models in production. Experience developing and shipping GenAI solutions utilising Large Language Models (LLMs), with an emphasis on API usage and Retrieval Augmented Generation (RAG). Proficient in Docker and cloud-based container orchestration services such as AWS Fargate, Google Cloud Run etc. You thrive working on ambiguous problems and More ❯

and prioritize AI applications that can drive operational efficiency and process improvements. Leverage state-of-the-art GenAI techniques, including Retrieval-Augmented Generation (RAG), AI agents, and other advanced methodologies, to develop robust AI models and solutions. Partner with business leaders and classic business teams to understand their needs and integrate AI solutions seamlessly … a strong focus on practical applications and real-world impact. Demonstrated expertise in GenAI techniques, including but not limited to Retrieval-Augmented Generation (RAG), AI agents, and other advanced AI methodologies. Strong fullstack development experience with TypeScript and React for frontend development coupled with FastAPI/Flask for Python backend services, with additional OfficeJS … API knowledge preferred Extensive knowledge of AI and machine learning ecosystems, including proficiency in modern RAG/Agent frameworks including Langchain/Langgraph, Llamaindex, Pydantic, Vercel AI SDK, and DSPY Strong analytical and problem-solving skills, with the ability to translate complex business requirements into effective AI solutions. Excellent collaboration and communication skills, with the ability to work effectively with More ❯

and prioritize AI applications that can drive operational efficiency and process improvements. Leverage state-of-the-art GenAI techniques, including Retrieval-Augmented Generation (RAG), AI agents, and other advanced methodologies, to develop robust AI models and solutions. Partner with business leaders and classic business teams to understand their needs and integrate AI solutions seamlessly … a strong focus on practical applications and real-world impact. Demonstrated expertise in GenAI techniques, including but not limited to Retrieval-Augmented Generation (RAG), AI agents, and other advanced AI methodologies. Strong fullstack development experience with TypeScript and React for frontend development coupled with FastAPI/Flask for Python backend services, with additional OfficeJS … API knowledge preferred Extensive knowledge of AI and machine learning ecosystems, including proficiency in modern RAG/Agent frameworks includingLangchain/Langgraph, Llamaindex, Pydantic, Vercel AI SDK, and DSPY Strong analytical and problem-solving skills, with the ability to translate complex business requirements into effective AI solutions. Excellent collaboration and communication skills, with the ability to work effectively with both More ❯

agent planning, tool use integration, multi-agent collaboration). AI Architecture: Deep understanding of AI/ML system architecture patterns, including microservices, event-driven architectures, and patterns specific to RAG (Retrieval-Augmented Generation), Graph RAG, Agentic RAG, and multi-agent systems. Vector Databases & Embeddings: Expertise in working with various embedding models and vector databases 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 ❯

grade systems. 🔍 What You’ll Be Working On Assist in the fine-tuning and evaluation of domain-specific LLMs , applying retrieval-augmented generation (RAG) and prompt engineering techniques. Contribute to the development of multi-agent systems using frameworks such as AutoGen , LangGraph , LangChain , or CrewAI . Support the integration of AI safety techniques into More ❯

platforms such as AzureML, Google Cloud, or AWS. Possess hands-on experience in using and developing LLM-powered applications, including retrieval-augmented generation (RAG), prompt engineering, and fine-tuning. Produce high-quality, coherent documentation to support their work, even though formal research output is not expected. Prior cybersecurity or business domain expertise is not More ❯

Job Description What will you be doing? This role presents an opportunity to engage deeply with MLOps, vector databases, and Retrieval-Augmented Generation (RAG) pipelines - skills that are in incredibly high demand. If you are passionate about shaping the future of AI and thrive on complex, high-impact challenges, we encourage you to apply. … data solutions, ensuring efficiency, scalability, and cost-effectiveness. Power Generative AI: Develop and manage specialized data flows for generative AI applications, including integrating with vector databases and constructing sophisticated RAG pipelines. Champion Data Governance & Ethical AI: Implement best practices for data quality, lineage, privacy, and security, ensuring our AI systems are developed and used responsibly and ethically. Tooling the Future … dataset versioning. Vector Database Experience: Practical experience working with vector databases (e.g., Pinecone, Milvus, Chroma) for embedding storage and retrieval. Generative AI Familiarity: Understanding of data paradigms for LLMs, RAG architectures, and how data pipelines support fine-tuning or pre-training. MLOps Principles: Familiarity with MLOps best practices for deploying and managing ML models in production. Data Governance & Ethics: Experience More ❯

to detail, and a collaborative, growth-focused mindset. Experience working in agile, product-driven engineering teams. Preferred Qualifications: Exposure to Retrieval-Augmented Generation (RAG) pipelines, vector databases (e.g., Pinecone, Weaviate, Milvus), and knowledge bases, with familiarity in integrating them with LLMs. Experience with advanced model monitoring, observability, and governance of LLMs and generative AI 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 ❯

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 ❯

when you meerkat. As Head of Data - Data Science & MLE, you'll lead our advanced analytics and machine learning teams, shaping strategy, driving delivery, and building the next generation of intelligent products. From dynamic pricing to agentic AI and LLMs, this is a pivotal role in bringing our AI strategy to life. This is more than a technical … machine learning and infrastructure-as-code tools (e.g. Terraform, CloudFormation) Exposure to advanced techniques such as large language models (LLMs), retrieval-augmented generation (RAG), and prompt engineering Demonstrated commitment to responsible AI, including experience with model explainability, fairness, or governance frameworks Why Join Us? You'll lead talented teams working on meaningful AI problems More ❯

projects, driving timelines, and delivering high-quality results in fast-paced environments. Bonus: Experience in deploying multi-agent frameworks and retrieval-augmented generation (RAG) pipelines. Familiarity with regulatory and privacy considerations in healthcare AI applications. IQVIA is a leading global provider of clinical research services, commercial insights and healthcare intelligence to the life sciences More ❯

rapid iteration, prompt engineering, and practical application. You'll fine-tune and optimize foundation models, craft sophisticated multi-agent systems, and invent novel solutions to power the next generation of voice intelligence. What You'll Do Integrate AI solutions into existing products and workflows Collaborate with cross-functional teams to understand business requirements and translate them into technical … AWS, Google Cloud, or Azure Knowledge of Kubernetes and containerization technologies Experience with data science and ML engineering Familiarity with retrieval-augmented generation (RAG) The requirements listed in the job descriptions are guidelines. You don't have to satisfy every requirement or meet every qualification listed. If your skills are transferable we would still More ❯

support our AI & data science solutions. This includes the application of Generative AI technologies, such as large language models (LLMs), Retrieval-Augmented Generation (RAG) pipelines, and prompt engineering-to build intelligent tools and enhance knowledge-based workflows. This is a fantastic opportunity for someone with foundational analytical expertise, eager to learn and grow in … client projects. You will be involved in: Applying data science best practices and standards across projects. Assisting in the design and implementation of Generative AI applications, including LLM workflows, RAG architectures, and prompt engineering. Collaborating with stakeholders to identify opportunities where GenAI can streamline tasks, automate insights, or improve decision-making. Gathering, processing, and managing data from disparate sources, ensuring … including ETL pipelines. Skills in data analysis, visualization, and storytelling with data. Analytical problem-solving and critical thinking abilities. Awareness of Generative AI techniques, including LLMs, prompt engineering, and RAG approaches. Experience with tools/frameworks for building Generative AI applications (e.g., agentic AI, orchestration frameworks, embedding models, vector databases). Understanding of machine learning and predictive modelling. Proficiency in More ❯

in AI Solution Architecture, including LLM/SLM (Large Language Models/Small Language Models) deployment, fine-tuning, inference optimization, retrieval-augmented generation (RAG), API-based AI deployment and model orchestration. Strong knowledge of Cloud AI & Hyperscalers, including AWS Bedrock, OpenAI, Google Vertex AI, Azure, hybrid and multimodal AI applications. Proficiency in Cloud Security 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 unlock massive scale for offline evaluation from third party datasets. Shape evaluation data to support future use cases like Retrieval-Augmented Generation (RAG) and natural language analytics. What we are looking for in our candidate Essential Proficiency in Python and SQL, with experience in frameworks like Pandas, PySpark, and NumPy for large-scale More ❯

with enterprise partners. No two weeks will look the same. Fine-tune and privately deploy LLMs - with a focus on Retrieval-Augmented Generation (RAG) pipelines Build and scale computer vision systems - from object detection to image segmentation Apply NLP to real-world business problems - summarisation, entity recognition, information extraction, and more Train and deploy More ❯

infrastructure, vector databases, or search systems. Experience building ML-powered products in production. Knowledge of large language models (LLMs) and retrieval-augmented generation (RAG). Public speaking or published technical content (talks, blog posts, tutorials). Familiarity with the Qdrant ecosystem or similar technologies. Benefits Competitive compensation and equity options. Flexible remote work environment. More ❯

existing AI integrations, develop new ones, and shape how AI powers our product. Youll work across the entire AI feature lifecycle: Architect and implement AI-powered capabilities: code generation, intelligent node creation, and workflow optimization Integrate LLM APIs and embedding models for text-to-workflow and natural language code suggestions Design and iterate on prompts to improve model … shipping and learning - fast iteration is second nature to you Bonus Points For Experience fine-tuning LLMs or working with retrieval-augmented generation (RAG) systems Frontend experience using Vue or React Technical writing or documentation contributions, especially around developer tools or AI n8n is an equal opportunity employer and does not discriminate on the More ❯

or PhD in Computer Science, Machine Learning, Computational Social Science, or a related quantitative field, or equivalent practical experience. (Desirable) Hands-on experience with advanced LLM application techniques like RAG, chain-of-thought, and agentic tool use. Deep experience in applying large language models (LLMs) to solve complex, open-ended problems. (Desirable) Experience designing and conducting experiments in a social … experimental design, and insights from computational social science to bring synthetic populations to life. Design AI Agents: Develop prompting strategies, retrieval-augmented generation (RAG), and tool-use frameworks for consistent personas, memories, and reasoning. Develop Experimental Methods: Design experiments to test behavioral outputs of LLM-powered agents against real data and social science principles. 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 ❯

Microservices, and AI integrations. Oversee LLM (Large Language Model) integration, leveraging models like GPT, and implement advanced AIarchitectures such as Retrieval-Augmented Generation (RAG) and Reinforcement Learning with HumanFeedback (RLHF). Lead the development of applications using React, .NET, Python, or Node.js, ensuring scalable, maintainable, andperformant solutions. Team Management: Manage and mentor engineering managers … React for front-end development. Deep knowledge of cloud technologies (AWS, Azure) and experience deploying cloud-native applications and Microservices architectures. Strong background in AI integration, specifically LLM technologies, RAG, and RLHF architectures, and the ability to apply these in client-facing solutions. Solid understanding of system design principles, including scalability, high availability, performance and security. Leadership & Client Engagement: Proven More ❯

AI integrations . Oversee LLM (Large Language Model) integration , leveraging models like GPT, and implement advanced AI architectures such as Retrieval-Augmented Generation (RAG) and Reinforcement Learning with Human Feedback (RLHF) . Lead the development of applications using React, .NET , Python , or Node.js , ensuring scalable, maintainable, and performant solutions. Team Management: Manage and mentor … for front-end development. Deep knowledge of cloud technologies (AWS, Azure) and experience deploying cloud-native applications and Microservices architectures . Strong background in AI integration , specifically LLM technologies , RAG , and RLHF architectures, and the ability to apply these in client-facing solutions. Solid understanding of system design principles , including scalability, high availability, performance and security. Leadership & Client Engagement: Proven More ❯