101 to 125 of 256 Retrieval-Augmented Generation Jobs in London

APIs, workflows, business systems and human‐in‐the‐loop processes Strong understanding of LLM capabilities and limitations, including prompt design, retrieval‐augmented generation, hallucination management, tool use, evaluation and monitoring Experience with AI governance and responsible AI, including agent governance, access controls, data protection ...

backend services. Full-stack development experience is a distinct advantage.* Extensive hands-on experience with **Generative AI concepts, Large Language Models (LLMs), transformer architectures, RAG, and advanced agentic frameworks** (e.g., LangChain, LangGraph, Google ADK. Optionally AutoGen, CrewAI, LlamaIndex, Semantic Kernel).* Deep comfort and practical experience with **containers and orchestration ...

backend services. Full‐stack development experience is a distinct advantage. Extensive hands‐on experience with Generative AI concepts, Large Language Models (LLMs), transformer architectures, RAG, and advanced agentic frameworks (e.g., LangChain, LangGraph, Google ADK. Optionally AutoGen, CrewAI, LlamaIndex, Semantic Kernel). Deep comfort and practical experience with containers and orchestration ...

Familiarity with AI/ML tools such as LangChain, Haystack, Hugging Face, Weaviate, or similar ecosystems. Hands-on experience with Retrieval Augmented Generation applications, AI agents & systems built around them. Experience using GenAI frameworks such as LlamaIndex, Crew AI, AutoGen, or similar agentic/… rapidly build & deploy AI solutions that solve high-priority business problems. Evaluate & integrate existing AI tools, frameworks, & APIs (e.g., LLMs, vector DBs, retrieval-augmented generation, AI agents) into robust applications. Champion automation in workflows—from data management ingestion & preprocessing to evaluation, to model integration ...

multi‐modal large language models across text, image, audio, and video Design and implement agentic workflows using prompt engineering, Retrieval‐Augmented Generation (RAG), APIs, and enterprise data integrations Fine‐tune, optimize, and deploy large language and multi‐modal models with a focus on scalability ...

agent system behind our AI doctor: reasoning, planning, tool use, clinical citation, and safe fallback behavior Retrieval & knowledge — vector infrastructure, embedding pipelines, RAG architecture over clinical literature and user health data. You know the difference between retrieval that works and retrieval that a clinician ...

widely across the organisation. Qualifications Strong software engineering background with modern AI technologies. Hands‐on experience building LLM‐powered systems in production, including agents, RAG pipelines, MCPs, tool‐use and multi‐step workflows. Familiarity with frameworks such as LangGraph, Pydantic AI or similar. Strong Python engineering skills and ability ...

coding standards and best practices Guide evaluation and safety approaches Support production deployments and troubleshooting Hands-on Skills Required Design multi-agent solutions Design RAG architecture Implement complex orchestration workflows Build scalable AI microservices Define AI governance patterns Troubleshoot production AI solutions ...

coding standards and best practices Guide evaluation and safety approaches Support production deployments and troubleshooting Hands-on Skills Required Design multi-agent solutions Design RAG architecture Implement complex orchestration workflows Build scalable AI microservices Define AI governance patterns Troubleshoot production AI solutions Technologies Azure OpenAI Azure AI Foundry Semantic Kernel ...

audit-focused position. Key Requirements Strong AI Security, Cloud Security or Security Architecture background. Hands-on experience securing LLMs, GenAI, RAG solutions and AI agents. Experience with Azure OpenAI, AI Foundry and ideally GCP Vertex AI. AI threat modelling, red teaming, prompt injection testing and security control design. Engineering ...

Skills Proven expertise and hands-on delivery of AI/ML in financial services Strong technical depth in GenAI/LLMs, prompt engineering, RAG architecture, fine-tuning, vector databases Knowledge of evaluation metrics for LLMs and safety/guardrail techniques Understanding of supervised learning and data engineering fundamentals Leadership experience ...

some of the following technologies or ecosystems: Amazon Bedrock and SageMaker Claude/Anthropic Azure AI Foundry or Azure OpenAI Databricks Vector databases and RAG architectures Agentic AI orchestration frameworks and MCP servers Model and data lifecycle management Desirable Experience Experience working within public sector or regulated environments Experience with ...

Skills: Proven expertise and hands-on delivery of AI/ML in financial services Strong technical depth in GenAI/LLMs, prompt engineering, RAG architecture, fine-tuning, vector databases Knowledge of evaluation metrics for LLMs and safety/guardrail techniques Understanding of supervised learning and data engineering fundamentals Leadership experience ...

trends within the software industry Built or integrated AI/ML features into products and systems, including recommendation engines, NLP, computer vision, predictive analytics, RAG pipelines, and agents Successfully led modernisation and replatforming efforts to align legacy systems with short and long-term business needs Led and delivered solutions ...

deploy AI solutions that solve high-priority business problems.* Evaluate and integrate existing AI tools, frameworks, and APIs (e.g., LLMs, vector DBs, retrieval-augmented generation, AI agents) into robust applications.* Champion automation in workflows—from data management ingestion and preprocessing to evaluation, to model … Familiarity with AI/ML tools such as LangChain, Haystack, Hugging Face, Weaviate, or similar ecosystems.* Hands-on experience with Retrieval Augmented Generation applications, AI agents and systems built around them.* Experience using GenAI frameworks such as LlamaIndex, Crew AI, AutoGen, or similar agentic ...

Engineering across the organisation. Responsibilities Defining engineering standards for enterprise AI Driving adoption of Agentic AI across multiple engineering teams Designing production-grade RAG architectures Establishing LangGraph and LangChain implementation patterns Designing scalable vector database retrieval strategies Leading best practice across MLOps, CI/CD and model deployment … observability Implementing guardrails and hallucination mitigation Influencing senior engineering stakeholders without direct line management Requirements Python AWS LangChain LangGraph Retrieval Augmented Generation (RAG) Vector databases MLOps Production Machine Learning platforms AI governance and engineering best practice PyTorch, TensorFlow or Scikit-learn Contract ...

organisation. You'll be responsible for Defining engineering standards for enterprise AI Driving adoption of Agentic AI across multiple engineering teams Designing production-grade RAG architectures Establishing LangGraph and LangChain implementation patterns Designing scalable vector database retrieval strategies Leading best practice across MLOps, CI/CD and model … engineering stakeholders without direct line management We're looking for someone with deep experience in: Python AWS LangChain LangGraph Retrieval Augmented Generation (RAG) Vector databases MLOps Production Machine Learning platforms AI governance and engineering best practice PyTorch, TensorFlow or Scikit-learn More important than ...

Builder and custom agents Power Automate Power Apps Dataverse Microsoft Fabric Power BI and OneLake SharePoint and Microsoft 365 Generative AI and retrieval-augmented generation APIs, connectors and enterprise integrations Who We're Looking For We are particularly interested in senior professionals from … Copilot Studio Custom agents, actions, workflows and knowledge sources Azure AI Foundry or Azure AI Services Generative AI and agentic AI concepts Retrieval-augmented generation Microsoft Fabric, Power BI or OneLake API, connector and enterprise integration design AI governance, security and responsible AI Change ...

scripting languages (e.g., Python) Author, optimize, and manage system prompts for customer-facing LLM systems Integrate API calls into Retrieval Augmented Generation (RAG) systems Evaluate model performance and annotation quality to produce reports for stakeholders Produce, process, and manipulate different types of language data … Familiarity with AI coding assistants Preferred Qualifications PhD in Applied Linguistics, Computational Linguistics, NLP, or related field Experience with SQL and Git Experience building RAG or agentic systems Experience conducting quantitative analysis Experience building data pipelines Experience with AWS services (Bedrock, S3, EC2, etc.) Knowledge of user experience concepts ...

token usage, model selection, caching approaches, and balancing trade-offs between latency, cost and output quality. Other Desired Skills Retrieval Augmented Generation (RAG) and vector database implementation, with practical experience using technologies such as OpenSearch, FAISS or similar to support scalable, production-ready retrieval ...

cybersecurity, email security, or adjacent enterprise SaaS domains preferred. Familiarity with AI/ML concepts, including prompt engineering, model evaluation, retrieval‐augmented generation, and agent orchestration frameworks, is valuable but can be developed in‐role. Prior experience in a developer enablement, developer experience, internal ...

Product management, AI Strategy and engineering Hands-on experience building and scaling AI or machine learning products in production environments. Experience understanding LLMs RAG Architecture and agentic workflows Excellent experience with Python. Excellent experience working with AI platforms (Claude, Open AI and AI orchestration frameworks) Experience within Financial services. ...

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. For more information about DRW's processing activities ...

optimisation, data processing) and cloud platforms (AWS, Azure or GCP). Hands‐on experience with a range of AI concepts such as LLMs, VLMs, RAG, agents, MCP, computer vision, GraphML, recommendation systems or reinforcement learning. Familiarity with ML frameworks (PyTorch, TensorFlow, etc.) and strong proficiency in Python and data structures ...

Python, Spark, Hive; and modern data platforms and tools (e.g. Databricks, NoSQL stores) and AI/ML frameworks (e.g. NLP). LLM integration patterns, RAG pipelines, and agentic AI architectures, including MCP server. Domain experience in Credit Risk, Market Risk, Liquidity or Capital. Experience in defining data strategy for advanced ...