23 of 23 Remote 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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

growth plans? Do you want to work in a team where innovation is a top priority? In this role you will: Develop and deploy AI-driven applications, integrating LLMs, RAG systems, and multi-agent frameworks. Build scalable data pipelines, APIs, and front-end interfaces to demonstrate real-world impact. Clean, transform, and model large structured and unstructured datasets. Collaborate with More ❯

team that’s pushing boundaries in autonomous AI What You’ll Need: A strong foundation in data science and machine learning Hands-on use of modern AI tools (LLMs, RAG, LangChain, co-pilots, agentic workflows) Curiosity and eagerness to learn in a fast-moving AI landscape Experience collaborating with stakeholders and translating business problems into technical solutions What You’ll More ❯

Vision/NLP Strong Software Engineering skills (3 years+) Developed LLM architecture and deployed LLM applications Uptodate with current trends in AI Some experience with applying latest techniques like RAG architecture, GenAI, Parallel training etc The role is hybrid, with adhoc requirements to be on client premises (London) this could be between 1-3 days a week, so we would More ❯

agents that democratise access to powerful computational biology tools, enabling researchers worldwide to leverage cutting-edge models via intuitive chat interfaces. Who We Are: We are building next-generation generative models that learn the fundamentals of biology. Our team pursues ambitious scientific goals with curiosity and a deep commitment to research excellence. Team members have previously contributed to … and frameworks such as LangChain or LlamaIndex, or you’ve built custom agent frameworks from scratch. You understand intelligent information retrieval, with experience in RAG (Retrieval-Augmented Generation), vector databases, and embedding models for knowledge extraction. You can architect complex workflows using orchestration tools such as Airflow, Prefect, or Temporal, or by More ❯

DevOps Working closely with platform leads, architects, and SRE teams to ensure stable, scalable operations Supporting benchmarking, evaluation, and experiment tracking to measure LLM performance and cost Contributing to RAG implementations and vector-driven retrieval patterns Helping shape platform patterns, reusable components, and clear documentation Troubleshooting performance issues across distributed systems and cloud services What You’ll Bring … S3, SQS, DynamoDB, Bedrock RESTful API development with FastAPI, microservices, Terraform, GitOps workflows Prompt evaluation tools such as Promptfoo SQL and NoSQL experience: MySQL, PostgreSQL, MongoDB, Cassandra Exposure to RAG patterns and vector search technologies What Success Looks Like: Secure, reusable GenAI components running smoothly in production Faster engineering delivery through automation and DevOps maturity High observability and strong evaluation More ❯

directly on several key product enhancements. You’ll work with Python/Django, Javascript and AWS. Our platform is also increasingly AI-powered, so familiarity with implementing AI solutions (RAG etc) is an advantage. You’ll be working on a project where everything is hosted in AWS and we have a lightweight automated deployment process. You’ll work alongside a …/or ReactJS. Working understanding of data capture and performance tracking, a willingness to contribute to design and UX decisions. AI familiarity with working on Gen AI projects, including RAG and API integration. Ability to work within project timelines, proactively communicate any delays and contribute to task re-prioritisation to keep things on track Experience in developing websites, web applications More ❯

/CDC) and entity resolution for graph population Author complex queries (Cypher, GSQL, AQL, SPARQL etc. depending on stack) Integrate knowledge graph retrieval & reasoning into LLM/RAG/GraphRAG systems Develop and evaluate graph ML/embedding models (link prediction, anomaly detection) Optimize graph performance, scaling, and query efficiency Liaise with client stakeholders: translate business problems into … TigerGraph, ArangoDB, OrientDB, or Stardog Proficiency in query languages (Cypher, GSQL, AQL, SPARQL, etc.) Strong background in pipelines, ETL, and entity resolution Exposure to integrating KG + LLM or RAG architectures Experience with graph algorithms, embeddings, or GNNs Cloud & production engineering literacy (AWS/Azure/GCP, containerization, CI/CD) Excellent communication skills — able to explain complex graph/ More ❯

Engineer , you’ll take ownership of core backend systems powering an advanced AI platform that integrates production-ready LLMs and agentic systems. You’ll architect scalable backend services, build RAG pipelines, and integrate AI components into distributed systems that enable real-world, production-scale AI deployment . 🔧 What You’ll Do Architect and develop backend systems (Python, FastAPI, Flask, NodeJS … Design and implement RAG pipelines (LangChain, Qdrant, embeddings) Build ETL & CI/CD workflows (Airflow, dbt, MLFlow) Integrate AI/LLM components into backend services Ensure reliability, scalability, and maintainability across systems Collaborate with a small, elite team of engineers and AI researchers 💼 About You 4+ years of backend experience (Python, distributed systems, async APIs) Proven experience integrating AI or More ❯

user-focused AI services across our business. What You’ll Do Design and implement a comprehensive AI testing and evaluation framework for all AI solutions, including LLM-based tools, RAG systems, and third-party platforms. Define and document quality standards for semantic accuracy, factual consistency, bias, tone, and relevance. Develop reusable testing templates, data sets, and evaluation methods that can … LLM behaviour (accuracy, hallucination, bias, tone, etc.). Familiarity with tools like Trulens, HumanLoop, PromptLayer, or similar; experience designing QA approaches for GenAI environments. Knowledge of modern AI architectures (RAG pipelines, embeddings, API integrations such as OpenAI, Azure OpenAI, Anthropic). Experience designing and implementing structured test regimes in fast-evolving contexts. Excellent communication and facilitation skills, engaging both technical More ❯

🚀 AI/Data Engineer (AI) 📍 Location: London (Hybrid – 1 every other week) 💼 up to £600 a day (Inside IR35) The Role As a Data Engineer , you’ll join our cross-functional Product team of software engineers, ML specialists, domain experts More ❯