26 to 50 of 140 Retrieval-Augmented Generation Jobs in the City of London

experts across 230 locales, to 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 to market 50% faster. Our mission More ❯

Mistral APIs LLM Frameworks: LangChain, LlamaIndex – for building LLM-powered applications Vector Databases: FAISS, Weaviate, Pinecone, Qdrant (Nice-to-Have) Retrieval-Augmented Generation (RAG): Experience building hybrid systems combining LLMs with enterprise data With a focus within Energy Trading, Oil & Gas, Financial Markets and Commodities, we offer a transparent Recruitment Service that has proven More ❯

Mistral APIs LLM Frameworks: LangChain, LlamaIndex – for building LLM-powered applications Vector Databases: FAISS, Weaviate, Pinecone, Qdrant (Nice-to-Have) Retrieval-Augmented Generation (RAG): Experience building hybrid systems combining LLMs with enterprise data With a focus within Energy Trading, Oil & Gas, Financial Markets and Commodities, we offer a transparent Recruitment Service that has proven More ❯

experts across 230 locales, to 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 to market 50% faster. Our mission More ❯

experts across 230 locales, to 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 to market 50% faster. Our mission More ❯

LLM, Computer Vision, NLP, Deep Learning Experience with deploying ML models into production An understanding of emerging technologies - such as Retrieval-Augmented Generation (RAG) and Knowledge Graphs A proactive mindset to identify problems and create areas for improvement Degree in Computer Science, AI, Big Data, or equivalent If interested, and the above applies to More ❯

LLM, Computer Vision, NLP, Deep Learning Experience with deploying ML models into production An understanding of emerging technologies - such as Retrieval-Augmented Generation (RAG) and Knowledge Graphs A proactive mindset to identify problems and create areas for improvement Degree in Computer Science, AI, Big Data, or equivalent If interested, and the above applies to More ❯

of web content Prototyping algorithms to optimise ad performance and bidding logic Applying modern LLM techniques — from prompt engineering to retrieval-augmented generation (RAG) Working cross-functionally with engineers, product and commercial teams to bring ideas to life What they’re looking for: 1–3 years’ experience in applied ML/AI (or equivalent More ❯

of web content Prototyping algorithms to optimise ad performance and bidding logic Applying modern LLM techniques — from prompt engineering to retrieval-augmented generation (RAG) Working cross-functionally with engineers, product and commercial teams to bring ideas to life What they’re looking for: 1–3 years’ experience in applied ML/AI (or equivalent More ❯

Software Engineer – Generative AI to design, develop, and deploy advanced AI solutions, including fine-tuning Vision and Language Models (V/LLMs) and implementing Graph-based RAG (Retrieval-Augmented Generation) systems. As a key member of our Global Supply Chain GenAI Team , you will drive the technical implementation of scalable, secure, and efficient Generative … experience in at least two AI domains such as NLP, NLG, Computer Vision, Machine Learning, or GenAI . Proven experience with fine-tuning and training LLMs , and developing Graph RAG solutions in production environments. Technical Expertise: Proficiency in cloud platforms – particularly Microsoft Azure and Databricks . Experience with tools and services such as: Azure Machine Learning, Azure AI Services, Azure More ❯

Software Engineer – Generative AI to design, develop, and deploy advanced AI solutions, including fine-tuning Vision and Language Models (V/LLMs) and implementing Graph-based RAG (Retrieval-Augmented Generation) systems. As a key member of our Global Supply Chain GenAI Team , you will drive the technical implementation of scalable, secure, and efficient Generative … experience in at least two AI domains such as NLP, NLG, Computer Vision, Machine Learning, or GenAI . Proven experience with fine-tuning and training LLMs , and developing Graph RAG solutions in production environments. Technical Expertise: Proficiency in cloud platforms – particularly Microsoft Azure and Databricks . Experience with tools and services such as: Azure Machine Learning, Azure AI Services, Azure 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 ❯

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 ❯

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 ❯

with productionising and deploying. Strong Python, SQL and programming skills Hands on experience working within areas such as NLP, LLM, recommendation systems Information Retrieval stacks, GenAI (specifically RAG systems). Familiar with architecture and implementation of 1+ ML frameworks (PyTorch, scikit-learn). Experience working in the full model lifecycle (including experimentation, training, testing, monitoring, and deployment). More ❯

with productionising and deploying. Strong Python, SQL and programming skills Hands on experience working within areas such as NLP, LLM, recommendation systems Information Retrieval stacks, GenAI (specifically RAG systems). Familiar with architecture and implementation of 1+ ML frameworks (PyTorch, scikit-learn). Experience working in the full model lifecycle (including experimentation, training, testing, monitoring, and deployment). More ❯

experience in AI/ML solutions on Azure, ideally with Generative AI. Expertise with Azure AI, Azure Machine Learning, and Azure OpenAI Service. Strong Python and PyTorch. Experience of RAG pipeline deployment, prompt engineering, fine-tuning foundation models. Knowledge of Responsible AI principles and AI governance. Strong understanding of cloud-native deployment practices. Excellent communication and stakeholder engagement skills. If More ❯

experience in AI/ML solutions on Azure, ideally with Generative AI. Expertise with Azure AI, Azure Machine Learning, and Azure OpenAI Service. Strong Python and PyTorch. Experience of RAG pipeline deployment, prompt engineering, fine-tuning foundation models. Knowledge of Responsible AI principles and AI governance. Strong understanding of cloud-native deployment practices. Excellent communication and stakeholder engagement skills. If More ❯

Must-Haves: Technical Chops 5+ years software engineering in production environments Strong in Node.js/TypeScript and Python Experience building microservices architectures Hands-on AI/ML integration (LLMs, RAG, agent frameworks) Cloud infrastructure expertise (GCP/AWS/Azure) Real-time systems and async processing experience Leadership 2+ years leading engineering teams (3-10 people) Proven track record hiring More ❯