26 to 50 of 76 Retrieval-Augmented Generation Jobs in London

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 ❯

in a distributed team environment. Customer-first mindset with a data-driven approach to problem-solving. Experience with large-scale distributed systems and incident response. Familiarity with LLM technologies (RAG, prompt engineering, evaluation methods). Knowledge of Azure services and Microsoft's ecosystem. Ability to work independently and collaboratively. Degree in Data Science, Computer Science, or related field (preferred). More ❯

e.g. React/Angular and integration with backend. AWS experience or working knowledge of AWS partner solutions like Databricks or Snowflake. Expertise of implementing GenAI in production including LLMs, RAG architectures, vector databases. Experience with automation and scripting (e.g., Terraform, Python). Knowledge of security and compliance standards (e.g., HIPAA, GDPR). Amazon is an equal opportunities employer. We believe More ❯

Own and deliver the roadmap end to end. Drive discovery to iteration, aligning cross functional squads and keeping initiatives on time and aligned to strategy. Integrate and optimize LLM, RAG, and tool calling pipelines for text. Define requirements and run evaluations to optimize our technology stack to text-based agents. Launch new channels into production. Work with clients and deployment More ❯

productionize generative AI models. Develop scalable GenAI pipelines that generate high-quality content, from product descriptions, reviews, titles, and other product content. Design and evaluate prompt tuning strategies and RAG systems to ensure factual and engaging outputs. Fine-tune foundation models and develop domain-specific adapters using techniques like LoRA, PEFT, and instruction tuning. Define best practices for model monitoring More ❯

driven architectures tailored to finance-specific use cases, including: Intelligent document processing (KYC/AML) Conversational finance assistants (customer service, advisory) Automated risk and compliance workflows Synthetic data generation for testing and modeling Personalized financial insights and reporting Select the right GenAI technologies (open-source, proprietary, cloud, or hybrid) to meet client goals without being tied to a … stakeholders in both business and technology. Experience working in or with regulated financial environments and navigating security and compliance requirements. Preferred Qualifications Experience with retrieval augmented generation (RAG), fine-tuning, model evaluation, or deploying models in production environments. Understanding of financial risk modeling, model validation, or operational risk frameworks. Knowledge of regulatory frameworks such More ❯

driven architectures tailored to finance-specific use cases, including: Intelligent document processing (KYC/AML) Conversational finance assistants (customer service, advisory) Automated risk and compliance workflows Synthetic data generation for testing and modeling Personalized financial insights and reporting Select the right GenAI technologies (open-source, proprietary, cloud, or hybrid) to meet client goals without being tied to a … stakeholders in both business and technology. Experience working in or with regulated financial environments and navigating security and compliance requirements. Preferred Qualifications Experience with retrieval augmented generation (RAG), fine-tuning, model evaluation, or deploying models in production environments. Understanding of financial risk modeling, model validation, or operational risk frameworks. Knowledge of regulatory frameworks such More ❯

Qualifications & Experience 10+ years in IT/Technology with 4+ years in AI/ML solution design and architecture. Proven experience with LLMs, AWS Bedrock, Agentic AI, LangGraph, MCP, RAG architectures, and MLOps. Strong background in enterprise architecture and integration with existing IT ecosystems. Telecom experience strongly preferred; other regulated/large-scale industries also considered. Excellent leadership, stakeholder management More ❯

and ML, whilst planning, developing, and maintaining production. What Youll Bring 3+ years experience in Generative AI experience Proven experience with language-based Generative AI eg. LangChain, LangSmith, LangGraph, RAG, Proficiency in Python, data science libraries such as numpy, Pandas, scikit-learn Experienced in LLM frameworks such as LangChain Ability to build and create prototypes in a fast-paced environment More ❯

incorporating new ideas and techniques into the team. What Youll Bring 3+ years experience in Generative AI experience Proven experience with language-based Generative AI eg. LangChain, LangSmith, LangGraph, RAG, Proficiency in Python, data science libraries such as numpy, Pandas, scikit-learn Experienced in LLM frameworks such as LangChain Strong client-facing consulting or B2B experience Ability to build and More ❯

Partnering with stakeholders to scope problems and identify the right solution - whether leveraging existing AI tools or building custom workflows & solutions. Designing and implementing agentic systems using techniques spanning RAG, grounding, prompt engineering, and orchestration on a GCP-first stack. Building and maintaining production ML pipelines and services for non-GenAI use cases (e.g. recommender systems, customer segmentation models, marketing … of MLOps - CI/CD, IaC (Terraform), experiment tracking, model registry, and monitoring. Proven experience deploying and operating ML systems in production (batch and real-time). Familiarity with RAG architectures, prompt engineering, and evaluation techniques. Strong grasp of security, privacy, and governance principles (IAM, secrets, PII handling). Excellent communication skills and ability to work with non-technical stakeholders. More ❯

NLP/ML/Knowledge Graph/GenAI systems for commercial applications Practical experience with traditional and state-of-the-art NLP methods, Knowledge Graph algorithms, and GenAI (including RAG and agentic frameworks) Experience writing production code Outstanding communication, problem-solving, and analysis skills Excellent coding skills in Python Proficiency with Git, AWS, Azure for remote model development and deployment More ❯

Architect multi-step agent workflows using: - Semantic Kernel SDK (C# or Python) - Azure OpenAI (GPT-4, function calling, chat completion) - Planner and Kernel Memory APIs for reasoning and memory - RAG pipelines grounded in enterprise data via Azure AI Search Microsoft 365 & Graph API Integration Enable agents to access and reason over content in SharePoint, OneDrive, Teams, Outlook, and Planner. utilise More ❯

Architect multi-step agent workflows using: - Semantic Kernel SDK (C# or Python) - Azure OpenAI (GPT-4, function calling, chat completion) - Planner and Kernel Memory APIs for reasoning and memory - RAG pipelines grounded in enterprise data via Azure AI Search Microsoft 365 & Graph API Integration Enable agents to access and reason over content in SharePoint, OneDrive, Teams, Outlook, and Planner. utilise More ❯

and liaise closely with downstream teams using the platform to build agentic applications. These applications range from consumer-facing chatbots to intelligent backend systems, and use techniques such as RAG and multimodal input/output. Your team has independence and the platform enables some of the most difficult problems at the intersection of our three-sided marketplace (riders, consumers, and More ❯

At least two ML/AI solutions delivered to production, ideally involving document understanding, NLP or search/retrieval systems · Practical knowledge and hands-on experience with: RAG architectures, LLMs (e.g., OpenAI, Antropic), Vector databases (e.g., FAISS, Azure AI Search), Embeddings (e.g., OpenAI) · Strong grasp of NLP techniques: named entity recognition (NER), document classification, chunking, summarization, question answering. More ❯

At least two ML/AI solutions delivered to production, ideally involving document understanding, NLP or search/retrieval systems · Practical knowledge and hands-on experience with: RAG architectures, LLMs (e.g., OpenAI, Antropic), Vector databases (e.g., FAISS, Azure AI Search), Embeddings (e.g., OpenAI) · Strong grasp of NLP techniques: named entity recognition (NER), document classification, chunking, summarization, question answering. More ❯

sector is looking for a full-stack developer with strong frontend capabilities, ideally in React . The candidate must have hands-on experience with AI/LLM technologies, including RAG, prompt engineering, or GenAI systems, with clear evidence of active contribution to such projects. The role offers flexible working arrangements, with travel to the client's offices in Windsor twice More ❯

a founder or founding engineer - you know what it means to balance craft, ownership, and speed Familiarity with tools that power today's AI agents: eval frameworks, agent tooling, RAG pipelines, and prompt engineering Prior experience with React, TypeScript, and/or Go Previous roles where you interfaced with customers or led technical projects with external stakeholders Our values Trust More ❯

a founder or founding engineer - you know what it means to balance craft, ownership, and speed Familiarity with tools that power today's AI agents: eval frameworks, agent tooling, RAG pipelines, and prompt engineering Prior experience with React, TypeScript, and/or Go Previous roles where you interfaced with customers or led technical projects with external stakeholders Our values Trust More ❯

a founder or founding engineer - you know what it means to balance craft, ownership, and speed Familiarity with tools that power today's AI agents: eval frameworks, agent tooling, RAG pipelines, and prompt engineering Prior experience with React, TypeScript, and/or Go Previous roles where you interfaced with customers or led technical projects with external stakeholders Our values Trust More ❯

learning through iteration and feedback Understanding of enterprise data governance, AI model integration, and scalable data architecture Familiarity with AI/ML technologies, including GenAI models (e.g., OpenAI GPT, RAG, fine-tuning models, or machine learning frameworks) Good communication skills, especially when collaborating across different functions or surfacing potential risks or questions Familiarity with tools like JIRA, Confluence, Excel, or More ❯

paced environment. Hands-on with Next.js, Typescript (React/Node), Tailwind, SQL (Prisma), and Azure . Bonus points for experience with: LLM APIs (OpenAI, Anthropic), Retrieval Augmented Generation pipelines, Python, serverless architecture, or Microsoft Office Add-ins. Strong product mindset and passion for building. You may have tried a startup yourself or hacked together More ❯

a SaaS product with a bolt-on chatbot. We're a pure-play AI company using cutting-edge techniques (think fine-tuned open source models, retrieval augmented generation, intelligent tool use) to actually automate real work, not just create more of it. And it works: Launched March 2024 $1M $15M revenue in since January More ❯

use cases Site search solutions (e.g. Yext, Algolia, Elasticearch) Headless CMS/DXP platforms (Contentful, Sitecore, AEM etc) REST/GraphQL APIs Modern Web Development & Accessibility Analytics platforms LLMs, RAG, and Graph Database concepts You must be a natural storyteller who simplifies complexity, engages executives, and builds trust quickly. You have an ability to develop business cases that communicate requirements More ❯