101 to 125 of 343 Retrieval-Augmented Generation Jobs in London

platforms, not research or PoC Deep background in intelligent document processing, OCR, or data extraction pipelines Technical leadership in designing retrieval-augmented generation (RAG) or agentic AI frameworks Experience deploying models in latency-sensitive, compliance-heavy environments Strong engineering foundation, Python, Kubernetes, Rust, vector databases, cloud-native deployment Experience building proprietary products Deep experience More ❯

platforms, not research or PoC Deep background in intelligent document processing, OCR, or data extraction pipelines Technical leadership in designing retrieval-augmented generation (RAG) or agentic AI frameworks Experience deploying models in latency-sensitive, compliance-heavy environments Strong engineering foundation, Python, Kubernetes, Rust, vector databases, cloud-native deployment Experience building proprietary products Deep experience More ❯

platforms, not research or PoC Deep background in intelligent document processing, OCR, or data extraction pipelines Technical leadership in designing retrieval-augmented generation (RAG) or agentic AI frameworks Experience deploying models in latency-sensitive, compliance-heavy environments Strong engineering foundation, Python, Kubernetes, Rust, vector databases, cloud-native deployment Experience building proprietary products Deep experience More ❯

platforms, not research or PoC Deep background in intelligent document processing, OCR, or data extraction pipelines Technical leadership in designing retrieval-augmented generation (RAG) or agentic AI frameworks Experience deploying models in latency-sensitive, compliance-heavy environments Strong engineering foundation, Python, Kubernetes, Rust, vector databases, cloud-native deployment Experience building proprietary products Deep experience More ❯

Mastery: Strong skills in Python, JavaScript, or similar languages. LLM & Agent Expertise: Hands-on experience with LLMs (OpenAI, Anthropic, Mistral) and agent frameworks. Advanced Tools: Familiarity with vector databases, RAG pipelines, prompt engineering, REST APIs, cloud deployment, and containerization (Docker/Kubernetes). Domain Knowledge: Experience in one or more business domains such as finance, housing, operations, or customer service. More ❯

across multiple industries. What You’ll Be 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 in cloud environments More ❯

across multiple industries. What You’ll Be 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 in cloud environments More ❯

across multiple industries. What You’ll Be 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 in cloud environments More ❯

across multiple industries. What You’ll Be 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 in cloud environments More ❯

on experience deploying ML models in production environments. Excellent programming skills in Python and familiarity with ML/DL libraries (TensorFlow, PyTorch, scikit-learn, Pandas). Practical experience with RAG or agentic AI frameworks (LangChain, LlamaIndex). Experience working with LLM APIs (e.g. Hugging Face, OpenAI). Exposure to conversational AI platforms (Dialogflow, Lex, Rasa, etc.). Ability to work More ❯

on experience deploying ML models in production environments. Excellent programming skills in Python and familiarity with ML/DL libraries (TensorFlow, PyTorch, scikit-learn, Pandas). Practical experience with RAG or agentic AI frameworks (LangChain, LlamaIndex). Experience working with LLM APIs (e.g. Hugging Face, OpenAI). Exposure to conversational AI platforms (Dialogflow, Lex, Rasa, etc.). Ability to work More ❯

on experience deploying ML models in production environments. Excellent programming skills in Python and familiarity with ML/DL libraries (TensorFlow, PyTorch, scikit-learn, Pandas). Practical experience with RAG or agentic AI frameworks (LangChain, LlamaIndex). Experience working with LLM APIs (e.g. Hugging Face, OpenAI). Exposure to conversational AI platforms (Dialogflow, Lex, Rasa, etc.). Ability to work More ❯

on experience deploying ML models in production environments. Excellent programming skills in Python and familiarity with ML/DL libraries (TensorFlow, PyTorch, scikit-learn, Pandas). Practical experience with RAG or agentic AI frameworks (LangChain, LlamaIndex). Experience working with LLM APIs (e.g. Hugging Face, OpenAI). Exposure to conversational AI platforms (Dialogflow, Lex, Rasa, etc.). Ability to work More ❯

on experience deploying ML models in production environments. Excellent programming skills in Python and familiarity with ML/DL libraries (TensorFlow, PyTorch, scikit-learn, Pandas). Practical experience with RAG or agentic AI frameworks (LangChain, LlamaIndex). Experience working with LLM APIs (e.g. Hugging Face, OpenAI). Exposure to conversational AI platforms (Dialogflow, Lex, Rasa, etc.). Ability to work More ❯

leading a small, high-performing team. What You'll Do Build and maintain data pipelines and cloud infrastructure (AWS/GCP). Develop AI-driven applications including LLM tools, RAG pipelines, and automation solutions. Lead on client communication , project delivery, and stakeholder alignment. Collaborate with Data Science and Analytics teams to improve data access, automation, and governance. Drive innovation through More ❯

leading a small, high-performing team. What You’ll Do Build and maintain data pipelines and cloud infrastructure (AWS/GCP). Develop AI-driven applications including LLM tools, RAG pipelines, and automation solutions. Lead on client communication , project delivery, and stakeholder alignment. Collaborate with Data Science and Analytics teams to improve data access, automation, and governance. Drive innovation through More ❯

enterprise use cases. Build and fine-tune LLM-based applications (chatbots, summarization, document Q&A, report generation, code assistants, etc.). Apply prompt engineering, RAG (Retrieval-Augmented Generation), and context-aware pipelines to ensure accuracy and relevance. Integrate AI models with enterprise systems, APIs, and data stores using Python, Java, or Node.js … . Ensure compliance with AI ethics, security, and governance standards. Prepare and curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (Pinecone, Weaviate, FAISS, Chroma) for semantic retrieval use cases. Partner with business stakeholders to identify and shape AI use cases. Contribute to More ❯

and TypeScript, Familiarity with Agentic Frameworks (e.g. LangChain, LangGraph) Proven experience building full stack applications using Git, SQL databases and modern deployment practices Understanding of LLMs, embedding models and RAG systems Solid software engineering fundamentals and secure coding practices Strong problem-solving, communication and teamwork skills At least 3 years hands-on engineering experience in project delivery environments Attention to More ❯

and TypeScript, Familiarity with Agentic Frameworks (e.g. LangChain, LangGraph) Proven experience building full stack applications using Git, SQL databases and modern deployment practices Understanding of LLMs, embedding models and RAG systems Solid software engineering fundamentals and secure coding practices Strong problem-solving, communication and teamwork skills At least 3 years’ hands-on engineering experience in project delivery environments Attention to More ❯

best practices across teams AI/ML Expertise Strong understanding of machine learning frameworks (TensorFlow, PyTorch, Scikit-learn) Experience with LLM integration (OpenAI, Anthropic, open-source models) Knowledge of RAG architectures, prompt engineering, and vector databases (Pinecone, Weaviate) Experience with MLOps tools and monitoring model performance in production Automation Architecture Deep knowledge of automation tools including GitHub Actions, Terraform, and More ❯

AI/LLM Engineer/Architect (Contract) - RAG … Implementation London (mostly remote) - Outside IR35 We're looking for a highly skilled AI/LLM Engineer/Architect to lead the implementation of Retrieval Augmented Generation (RAG) solutions for a major client project in London. This is a hands-on contract role for someone who can build from the ground up - shaping the … steer the organisation's AI stack, define best practices, and ensure the delivery of scalable, cost-effective solutions. What You'll Do • Lead the design, build, and deployment of RAG solutions • Drive Agentic development and intelligent automation • Provide guidance on AI architecture and technology strategy • Build and deploy scalable machine learning applications on AWS • Apply ML Ops tooling and workflows More ❯

AI/LLM Engineer/Architect (Contract) - RAG … Implementation| London (mostly remote) - Outside IR35 We're looking for a highly skilled AI/LLM Engineer/Architect to lead the implementation of Retrieval Augmented Generation (RAG) solutions for a major client project in London. This is a hands-on contract role for someone who can build from the ground up - shaping the … steer the organisation's AI stack, define best practices, and ensure the delivery of scalable, cost-effective solutions. What You'll Do Lead the design, build, and deployment of RAG solutions Drive Agentic development and intelligent automation Provide guidance on AI architecture and technology strategy Build and deploy scalable machine learning applications on AWS Apply ML Ops tooling and workflows More ❯

AI/LLM Engineer/Architect (Contract) - RAG … Implementation| London (mostly remote) - Outside IR35 We're looking for a highly skilled AI/LLM Engineer/Architect to lead the implementation of Retrieval Augmented Generation (RAG) solutions for a major client project in London. This is a hands-on contract role for someone who can build from the ground up - shaping the … steer the organisation's AI stack, define best practices, and ensure the delivery of scalable, cost-effective solutions. What You'll Do Lead the design, build, and deployment of RAG solutions Drive Agentic development and intelligent automation Provide guidance on AI architecture and technology strategy Build and deploy scalable machine learning applications on AWS Apply ML Ops tooling and workflows More ❯

AI/LLM Engineer/Architect (Contract) - RAG … Implementation| London (mostly remote) - Outside IR35 We're looking for a highly skilled AI/LLM Engineer/Architect to lead the implementation of Retrieval Augmented Generation (RAG) solutions for a major client project in London. This is a hands-on contract role for someone who can build from the ground up - shaping the … steer the organisation's AI stack, define best practices, and ensure the delivery of scalable, cost-effective solutions. What You'll Do • Lead the design, build, and deployment of RAG solutions • Drive Agentic development and intelligent automation • Provide guidance on AI architecture and technology strategy • Build and deploy scalable machine learning applications on AWS • Apply ML Ops tooling and workflows More ❯

AI/LLM Engineer/Architect (Contract) - RAG … Implementation| London (mostly remote) - Outside IR35 We're looking for a highly skilled AI/LLM Engineer/Architect to lead the implementation of Retrieval Augmented Generation (RAG) solutions for a major client project in London. This is a hands-on contract role for someone who can build from the ground up - shaping the … steer the organisation's AI stack, define best practices, and ensure the delivery of scalable, cost-effective solutions. What You'll Do • Lead the design, build, and deployment of RAG solutions • Drive Agentic development and intelligent automation • Provide guidance on AI architecture and technology strategy • Build and deploy scalable machine learning applications on AWS • Apply ML Ops tooling and workflows More ❯