101 to 125 of 236 Retrieval-Augmented Generation Jobs in London

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 ...

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 ...

Financial Crime, customer onboarding, and real‐time decision management. Experience operationalising AI/ML solutions, including model lifecycle management, feature engineering, vector databases, RAG architectures, AI governance and production‐scale MLOps. Knowledge of payment, acquiring, merchant services, consumer lending, regulatory compliance, model risk management and responsible AI within highly regulated ...

cloud platform engineering who are looking to deepen their AI expertise. Understanding and hands‐on experience with LLM based applications and related technologies (e.g. RAG, vector databases, agent frameworks). Experience managing and deploying AI Agent and Knowledge Management systems is a plus (e.g Glean, Gemini Enterprise, Amazon Quick, Writer ...

time decision management Some other highly valued skills may include: Experience operationalising AI/ML solutions including model lifecycle management, feature engineering, vector databases, RAG architectures, AI governance and production‐scale MLOps Experience building modern data platforms incorporating data mesh/Lakehouse architectures, real‐time analytics, graph technologies, observability ...

prototypes, products and production ready solutions Designing and implementing end to end AI solutions that integrate with enterprise systems Working with LLMs, prompt engineering, RAG patterns, embeddings and fine tuning Developing AI agents and agentic workflows using modern frameworks Working with vector databases, APIs and modern data platforms Supporting … collaborative, high performing environment Experience Required: Strong Python and SQL experience Applied AI engineering, ML engineering or software engineering background Experience with LLMs, RAG, embeddings, prompt engineering or fine tuning Exposure to LangChain, LangGraph, Agent Development Kit or similar agent frameworks Experience with vector databases such as Pinecone, Chroma ...

development, building scalable, resilient, and production-grade systems Hands-on experience developing AI-powered applications, including large language models (LLMs), AI agents, retrieval-augmented generation, prompt engineering, evaluation frameworks, and model optimization techniques Proven ability to take AI solutions from concept and experimentation through … power AI applications, intelligent automation solutions, and AI agents Develop and deploy advanced LLM‐powered applications using techniques such as retrieval‐augmented generation, prompt engineering, evaluation frameworks, and model optimization Create robust data and inference pipelines that support both real‐time and batch ...

Identifying opportunities to adopt AI into new or existing digital servicesLeading AI solution design using modern patterns such as retrieval‐augmented generation (RAG), multi-agent orchestration, vector search, prompt chaining, and model fine‐tuning.Demonstrating hands-on ability to assess technical feasibility of AI concepts … services, DevOps and waterfall environments before and know the challenges to bring the best of old and new worldAbility to develop our next generation of digital architects through formal line management and informal mentoring, coaching and learning developmentWHAT YOU'LL LOVE ABOUT WORKING HEREOur Digital Architects ...

egress controls). Strong understanding of AI/LLM capabilities and risks across the lifecycle (model onboarding/ingestion, retrieval/RAG patterns, model serving) and associated control points (traceability, access, data handling), including assessing control design and operational effectiveness in fast‐changing environments. Experience with agentic ...

Anthropic APIs. The role requires 3-5 years of experience, strong Python/TypeScript skills, and hands-on experience with LLMs, vector databases, and RAG pipelines. #J-18808-Ljbffr ...

multi-agent orchestration (beyond simple chatbots) Hands-on experience with LLM guardrails , cost optimization , and model evaluation frameworks Deep understanding and implementation experience in: RAG architectures Vector stores Embeddings Prompt and response evaluation Strong engineering hygiene with experience in:Automated testingCI/CD pipelinesObservability & monitoringCorrelation IDs and tracingContainerization with Docker ...