1 to 25 of 71 Retrieval-Augmented Generation Jobs in Central London

/ML systems for business intelligence. Build robust APIs, microservices, and data pipelines to power intelligent, data-driven tools. Develop retrieval-augmented generation (RAG) systems using vector databases for contextual AI. Set the technical direction for backend and AI integration best practices. Partner with cross-functional teams to identify and deliver high-value AI … the ability to translate technical outcomes into business impact. Tech Environment Languages: Python, TypeScript, Java AI/LLM: OpenAI, Anthropic, Retrieval-Augmented Generation (RAG) Infrastructure: AWS (Lambda, ECS, S3), Terraform, Docker Databases: PostgreSQL, MySQL, Redis, vector databases DevOps: GitHub, CI/CD pipelines Why Join Competitive salary and comprehensive benefits package 25 days annual More ❯

/ML systems for business intelligence. Build robust APIs, microservices, and data pipelines to power intelligent, data-driven tools. Develop retrieval-augmented generation (RAG) systems using vector databases for contextual AI. Set the technical direction for backend and AI integration best practices. Partner with cross-functional teams to identify and deliver high-value AI … the ability to translate technical outcomes into business impact. Tech Environment Languages: Python, TypeScript, Java AI/LLM: OpenAI, Anthropic, Retrieval-Augmented Generation (RAG) Infrastructure: AWS (Lambda, ECS, S3), Terraform, Docker Databases: PostgreSQL, MySQL, Redis, vector databases DevOps: GitHub, CI/CD pipelines Why Join Competitive salary and comprehensive benefits package 25 days annual More ❯

Responsibilities Architect Autonomous Agents: Design and implement robust, goal-driven AI agents using leading frameworks like LangChain, LangGraph, and the Google Agent Development Kit (ADK). Develop and Evaluate RAG Pipelines: Engineer and optimize end-to-end Retrieval-Augmented Generation (RAG) systems, including data ingestion, chunking strategies, and implementing rigorous pipeline evaluation frameworks for … Kit (ADK) LLM Expertise: Advanced Prompt Engineering and hands-on experience with model fine-tuning techniques including PEFT and QLoRA. Proven experience with models like Gemini, and Llama 3. RAG & Vector Databases: Deep expertise in RAG architecture and evaluation metrics. Proven experience with Vector Databases such as Milvus, Pinecone, or Chroma. Software & Cloud Engineering: Programming & APIs: Expert-level Python and More ❯

tune LLM-based applications such as: Chatbots Document Q&A systems Report generators Code assistants Summarization tools Apply prompt engineering, Retrieval-Augmented Generation (RAG), and context-aware pipelines to enhance model accuracy and relevance. Integrate AI models with enterprise systems, APIs, and data stores using Python, Java, or Node.js. Collaborate with architects to define … optimization. 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 (e.g., Pinecone, Weaviate, FAISS, Chroma) for semantic search and retrieval. Partner with business stakeholders to identify and shape impactful AI use cases. Contribute to … Node.js. Hands-on experience with LLMs (e.g., GPT, LLaMA, Claude, Mistral), Transformers, and Diffusion models. Experience with Hugging Face Transformers, LangChain, LLM orchestration frameworks, and prompt tuning. Familiarity with RAG pipelines, embedding models, and vector databases. Experience with cloud platforms (AWS, GCP, Azure) and AI/ML services. Knowledge of MLOps tools and practices (e.g., MLflow, Kubeflow, Vertex AI, Azure More ❯

tune LLM-based applications such as: - Chatbots - Document Q&A systems - Report generators - Code assistants - Summarization tools Apply prompt engineering, Retrieval-Augmented Generation (RAG), and context-aware pipelines to enhance model accuracy and relevance. Integrate AI models with enterprise systems, APIs, and data stores using Python, Java, or Node.js. Collaborate with architects to define … optimization. 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 (e.g., Pinecone, Weaviate, FAISS, Chroma) for semantic search and retrieval. Partner with business stakeholders to identify and shape impactful AI use cases. Contribute to … Node.js. Hands-on experience with LLMs (e.g., GPT, LLaMA, Claude, Mistral), Transformers, and Diffusion models. Experience with Hugging Face Transformers, LangChain, LLM orchestration frameworks, and prompt tuning. Familiarity with RAG pipelines, embedding models, and vector databases. Experience with cloud platforms (AWS, GCP, Azure) and AI/ML services. Knowledge of MLOps tools and practices (e.g., MLflow, Kubeflow, Vertex AI, Azure More ❯

tune LLM-based applications such as: - Chatbots - Document Q&A systems - Report generators - Code assistants - Summarization tools Apply prompt engineering, Retrieval-Augmented Generation (RAG), and context-aware pipelines to enhance model accuracy and relevance. Integrate AI models with enterprise systems, APIs, and data stores using Python, Java, or Node.js. Collaborate with architects to define … optimization. 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 (e.g., Pinecone, Weaviate, FAISS, Chroma) for semantic search and retrieval. Partner with business stakeholders to identify and shape impactful AI use cases. Contribute to … Node.js. Hands-on experience with LLMs (e.g., GPT, LLaMA, Claude, Mistral), Transformers, and Diffusion models. Experience with Hugging Face Transformers, LangChain, LLM orchestration frameworks, and prompt tuning. Familiarity with RAG pipelines, embedding models, and vector databases. Experience with cloud platforms (AWS, GCP, Azure) and AI/ML services. Knowledge of MLOps tools and practices (e.g., MLflow, Kubeflow, Vertex AI, Azure More ❯

about building intelligent, agentic systems using LangGraph. If you thrive at the intersection of LLMs, automation, and complex system design, this is your chance to shape the next generation of AI infrastructure. What You’ll Do Design, implement, and optimize LangGraph-based AI workflows and multi-agent systems Integrate LLMs, APIs, and data pipelines into production-ready solutions … contributions to the open-source project) Strong background in Python, LangChain, OpenAI APIs, and LLM architectures Familiarity with vector databases, retrieval-augmented generation (RAG), and prompt engineering Understanding of software design principles, version control (Git), and CI/CD practices Creative problem-solver with a bias toward action and experimentation Nice to Have Experience More ❯

innovation and deliver impactful solutions. Key Responsibilities: - Data & Retrieval: Build ingestion pipelines for structured and unstructured data; design retrieval-augmented generation (RAG) systems; manage vector and keyword indexes; develop NLP and recommendation systems; implement metadata and tagging frameworks. - LLM & ML Applications: Develop and maintain ML and LLM models; build LLM apps with … consumer-focused platforms is desirable. - Keen awareness of AI/ML industry trends and best practices. If you’re a hands-on AI engineer looking to shape next-generation research platforms, please send your CV and a brief introduction today. Important: This role does not offer visa sponsorship; applicants must have the right to work in the UK. More ❯

Model Context Protocol for managing context and tool interfaces for agents. LLM integration patterns, including prompt orchestration and tool calling. Retrieval-Augmented Generation (RAG) for dynamic context injection. Understanding of user-centric design for AI interfaces and intelligent automation. Experience with AI frameworks (PyTorch, Tensorflow, Hugging Face etc.). Preferred Skills Preferred knowledge of More ❯

Date: ASAP About the Opportunity: We are seeking a highly capable and intellectually curious Junior AI Engineer/Developer to join a fast-growing fintech company building next-generation AI infrastructure for financial services. This role is designed for a recent AI-focused Master’s graduate from a leading university who wants to move beyond academic models and … and capital-markets workflows into well-scoped AI problems and measurable targets Contributing to internal R&D on LLM evaluation, retrieval-augmented generation (RAG), and methods for improving reliability and explainability of models in financial contexts What We’re Looking For: A recently completed AI-focused Master’s degree from a top-tier university … tuning, and evaluating models using real datasets (not just toy examples), including careful validation and error analysis Familiarity with modern LLM tooling and workflows (e.g. using APIs, building simple RAG or prompt-based systems) is highly advantageous Comfortable working in Linux-based development environments, using Git, testing, and basic CI practices A structured, “extended thinking” mindset: you enjoy breaking down More ❯

a remote-first team Willingness to travel occasionally for in-person collaboration or client work Nice to Have Experience with retrieval-augmented generation (RAG) or foundation models Exposure to NLP, recommendation systems, or time series forecasting Familiarity with streaming architectures and experimentation platforms Understanding of healthcare data standards (HIPAA, FHIR) Interest in ethical AI More ❯

root cause analyses. Build and scale machine learning algorithms and pipelines to production using big data technologies. Develop and deploy retrieval-augmented generation (RAG) systems and LLM-based applications. Design and evaluate A/B tests and communicate results across cross-functional teams. Define, implement, and monitor key performance metrics for AI-driven product 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 ❯

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 ❯

Overview IDC is building the next generation of intelligent, AI-powered platforms that transform how technology decisions get made. This confidential initiative reimagines the way decision-makers discover and interact with trusted research and data—and will be foundational to IDC’s future. We are looking for a Principal Product Manager – Technical (PM-T) to lead the product … adoption, engagement, and measurable business outcomes. Preferred Qualifications : Experience building or scaling AI/ML-powered products, especially involving search, retrieval-augmented generation (RAG), or entity extraction. Familiarity with knowledge graph design, semantic modeling, or enterprise data platforms. Background working with structured research, metadata systems, or content syndication at scale. Experience with enterprise SaaS 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 ❯

AI/ML services. Strong foundation in software engineering principles for building scalable, maintainable, and production-ready AI systems. Experience in designing and implementing enterprise-grade AI solutions, including RAG-based solutions with LLMs and vector databases (e.g., Pinecone, Weaviate, FAISS). Proven experience in full stack development and AI/ML system implementation within enterprise environments. Strong grasp of More ❯

Hands-on exposure to AWS (e.g. EC2, S3, IAM; bonus points for Lambda, ECS/EKS, SageMaker, or Bedrock) Familiarity with LLM frameworks and tooling (e.g. LangChain, vector databases, RAG pipelines) is highly advantageous Genuine interest in AI compliance, governance, and emerging regulation (e.g. EU AI Act, model risk, responsible AI) Strong problem-solving mindset with a passion for building 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 ❯

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 ❯

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 ❯

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 ❯

principles and software testing practices. Experience delivering customer-facing products and supporting the full development lifecycle. Excellent communication, stakeholder engagement, and advisory skills. Desirable Experience with Generative AI, LLMs, RAG, LangChain, or Semantic Kernel. Familiarity with Microsoft Power Platform. Front-end experience with React, Angular, Vue.js, Flutter, or Progressive Web Apps. Exposure to edge computing, VR/AR, or robotics. More ❯