1 to 25 of 79 Retrieval-Augmented Generation Jobs in the City of London

focused on building the intelligence layer of advanced AI products. You will design and implement Retrieval-Augmented Generation (RAG) pipelines, agentic workflows, and robust evaluation frameworks that ensure AI systems are accurate and aligned with real-world business needs. Reporting to the Director … with AI Platform Engineering to ensure solutions are scalable, maintainable, and production ready. Design, build, and train production-grade generative AI systems. Implement robust RAG pipelines that effectively leverage proprietary and domain-specific content. Develop agentic and workflow-based AI patterns with a focus on determinism, quality, and maintainability. Apply ...

field. Demonstrable experience of deploying modern AI/ML solutions into production, including prompt engineering, retrieval-augmented generation (RAG), model evaluation, and monitoring using metrics (e.g. precision, recall, NDCG and drift detection). Strong Python skills with a grounding in software engineering best practices ...

Chatbots - Document Q&A systems - Report generators - Code assistants - Summarization tools Apply prompt engineering, Retrieval-Augmented Generation (RAG), and context-aware pipelines to enhance model accuracy and relevance. Integrate AI models with enterprise systems, APIs, and data stores using Python, Java, or Node.js. Collaborate … LLMs (e.g., GPT, LLaMA, Claude, Mistral), Transformers, and Diffusion models. Experience with Hugging Face Transformers, LangChain, LLM orchestration frameworks, and prompt tuning. Familiarity with RAG pipelines, embedding models, and vector databases. Experience with cloud platforms (AWS, GCP, Azure) and AI/ML services. Knowledge of MLOps tools and practices (e.g. ...

help drive innovation across cutting-edge AI initiatives — from chatbots and voice assistants to advanced retrieval-augmented generation (RAG) systems and agentic workflows. The Role You’ll work closely with the AI Engineering and Data Science teams to: Develop and prototype AI-driven solutions … across customer-facing and internal applications. Build and optimise LLM-based assistants , RAG pipelines , and agentic AI workflows . Collaborate on the architecture and deployment of scalable AI solutions (with support from engineering). Partner with stakeholders to translate business needs into practical, intelligent systems. Mentor junior team members ...

help drive innovation across cutting-edge AI initiatives — from chatbots and voice assistants to advanced retrieval-augmented generation (RAG) systems and agentic workflows. The Role You’ll work closely with the AI Engineering and Data Science teams to: Develop and prototype AI-driven solutions … across customer-facing and internal applications. Build and optimise LLM-based assistants , RAG pipelines , and agentic AI workflows . Collaborate on the architecture and deployment of scalable AI solutions (with support from engineering). Partner with stakeholders to translate business needs into practical, intelligent systems. Mentor junior team members ...

Responsibilities: Design, build, and deploy Large Language Model (LLM)-driven applications into production Develop retrieval-augmented generation (RAG) systems for real-time insights from performance and wearable data Optimise and fine-tune LLMs for efficiency, scalability, and accuracy in production environments Build and maintain … software engineering fundamentals Proven experience working with LLMs in production (fine-tuning, prompt engineering, or API integrations) Proficiency in Python Experience designing and deploying RAG pipelines (vector databases, embeddings, retrieval optimisation) Academic Excellence: BSc or MSc in Computer Science, AI, Data Science, or related technical field from ...

with the team to develop GenAI proof-of-concepts (POCs) for clients using technologies like Retrieval-Augmented Generation (RAG) and intelligent agents. Scale existing POCs to production-ready solutions for customer use. Design and develop Full Stack applications for both GenAI and non-GenAI ...

with the team to develop GenAI proof-of-concepts (POCs) for clients using technologies like Retrieval-Augmented Generation (RAG) and intelligent agents. Scale existing POCs to production-ready solutions for customer use. Design and develop Full Stack applications for both GenAI and non-GenAI ...

with the team to develop GenAI proof-of-concepts (POCs) for clients using technologies like Retrieval-Augmented Generation (RAG) and intelligent agents. Scale existing POCs to production-ready solutions for customer use. Design and develop Full Stack applications for both GenAI and non-GenAI ...

Software Engineer | Python | RAG | Retrieval Augmented Generation | DAG | Dagster | London, UK The role We are hiring an agent-focused software engineer to build internal agentic frameworks for our discovery product. You will define and implement the operating system that allows scientists to run repeatable … Ability to communicate clearly across disciplines and translate real scientific workflows into robust software. Nice-to-have experience Experience with agent frameworks, retrieval-augmented generation, or multi-agent systems. Familiarity with ML experiment tracking or model registries and data orchestration platforms. Exposure to knowledge ...

environments. This is an end-to-end role focusing on the design and implementation of applied AI capabilities, from building secure knowledge-based chat (RAG solutions) over internal documentation and codebases, to developing complex agentic workflows that integrate deeply within their product suite. Key Responsibilities Define and execute … core business objectives. Implement documentation ingestion pipelines for structured knowledge bases. Build secure, enterprise-grade Retrieval-Augmented Generation (RAG) systems. Collaborate with engineering teams to embed AI features into existing and new products. Design and deploy agentic workflows to automate multi-step tasks across ...

engineering and data discovery for model development. Familiarity with AI tooling such as fine-tuning, retrieval-augmented generation (RAG), and agentic workflows. Excellent communication skills with the ability to simplify technical content for business audiences. Willingness to travel occasionally for client meetings (approx. ...

engineering best practices, delivery processes, and a high-performance team culture. Technical Ownership Lead architecture and development of LLM-powered and agentic applications , including RAG systems, chat/workflow agents, and domain-specific AI services. Drive technical direction with sound judgement and pragmatic decision-making. Build and deploy production-grade … systems into production. Deep expertise in: LLMs & transformer-based models Agentic frameworks (e.g., LangChain, LangGraph) Retrieval-Augmented Generation (RAG) Python Building or integrating LLMs into products or workflows Working with GenAI frameworks and tooling Designing or optimising prompt pipelines Deploying or supporting GenAI features ...

foundation in software engineering principles for building scalable, maintainable, and production-ready AI systems. Experience in designing and implementing enterprise-grade AI solutions, including RAG-based solutions with LLMs and vector databases (e.g., Pinecone, Weaviate, FAISS). Bachelor's or Master's degree in Computer Science, Engineering, or a related ...

foundation in software engineering principles for building scalable, maintainable, and production-ready AI systems. Experience in designing and implementing enterprise-grade AI solutions, including RAG-based solutions with LLMs and vector databases (e.g., Pinecone, Weaviate, FAISS). Proven experience in full stack development and AI/ML system implementation within ...

create high-quality pre-trained datasets, fine-tuned industry-specific Large Language Models(LLMs), and Retrieval-Augmented Generation (RAG) pipelines supported by vector databases. Our innovations can reduce Generative Artificial Intelligence(Gen AI) costs by up to 80% and bring Gen AI solutions ...

problem-solving and technical storytelling abilities. Commitment to ethical AI deployment. Key Skills Advanced Python and backend languages. Expertise in prompt engineering, fine-tuning, RAG, and agentic design. Familiarity with AI tools (GitHub Copilot, ChatGPT, SonarQube, LangChain, etc.). Understanding of observability, security, and compliance in AI systems. Experience with ...

Doing: Designing, developing, and deploying machine learning and AI models Designing, developing, and deploying LLM applications (e.g. GPT, LLaMA, Claude) integrated with RAG pipelines Implementing end-to-end workflows: from data acquisition, cleaning, and feature engineering to model training, deployment, and monitoring Building scalable pipelines and APIs for AI services ...

Python and familiarity with deep learning/NLP libraries (LangChain, PyTorch, TensorFlow, HuggingFace Transformers). Experience with building Q&A systems and retrieval-augmented generation pipelines. Knowledge of vector databases or semantic search concepts. Familiarity with cloud AI platforms (AWS Bedrock, Azure OpenAI … deploying AI workloads to Azure Container Apps (ACA), Azure Kubernetes Service (AKS), or serverless functions (Azure Functions) for event-driven agent triggers. Experience implementing RAG using Azure AI Search (vector, semantic, and hybrid search) and OneLake/Microsoft Fabric. Nice to Have Skills Certification: Microsoft Certified: Azure AI Engineer Associate ...

/Vertex AI , including fine-tuning, vector search, and low-latency inference. Build end-to-end LLM applications , leveraging RAG (Retrieval-Augmented Generation) , agentic workflows, and prompt engineering. Implement robust evaluation frameworks to monitor LLM quality, hallucinations, token usage, and content safety. Develop … Looking For Essential 5+ years' experience in machine learning engineering or applied AI roles. Recent, demonstrable experience with LLMs, Generative AI, and/or RAG-based systems . Strong Python skills using frameworks such as PyTorch, TensorFlow, Hugging Face, or Google GenAI . Experience with vector databases and retrieval ...

from experimentation into live systems Experience with MLOps tools (MLflow, Weights & Biases, DVC, or similar) Hands-on exposure to LLM/GenAI applications, including RAG Nice to have: Experience with advanced LLM techniques (e.g. agents, tool use) Familiarity with vector databases and feature stores Experience with Docker, Kubernetes, or Infrastructure ...

shape the organisation’s LLM strategy, conversational AI capabilities, and broader applied AI roadmap. Key Responsibilities Design, build, and enhance LLM-driven tools, including RAG pipelines, embeddings, and prompt engineering. Support and expand the organisation’s chatbot capabilities. Work with unscripted data (text, call transcripts) to define key insights ...

Proven ability to transition validated models into production-ready systems with appropriate governance Exposure to LLM/Generative AI applications, including prompt engineering and RAG architectures Ability to communicate complex technical concepts clearly to non-technical stakeholders Experience working in cross-functional teams to deliver production solutions Eligibility and willingness ...

Services industry. Experience with Generative AI, Large Language Models (LLMs), and advanced frameworks like: LangChain, LangGraph, Haystack LlamaIndex, CrewAI, AutoGen, Transformers (Hugging Face) and RAG pipelines. Proven experience in leading teams and managing complex AI projects. Expertise in machine learning, data modelling, and advanced statistical techniques. Strong programming skills ...

Architect & Engineer AI Systems: You won’t just call an API; you will build robust processing architectures that leverage off-the-shelf LLMs, RAG pipelines, and context engineering to solve hard problems. You will benchmark these systems rigorously to ensure they actually work, not just "feel" smart. Build the Backend ...