17 of 17 Retrieval-Augmented Generation Jobs in East London

architecture of LLMs. Foundational knowledge of diffusion models for image generation. Can display and present completed project/s using LLMs with a focus on any of the following: RAG, Agentic-RAG, fine-tuning Some experience or familiarity with deploying applications in the Cloud using services such as AWS or Azure. Proven track record in securing web/API applications. More ❯

and US investors . Our founders have delivered cutting-edge AI at world-class research labs and high-growth technology companies. Now, operating in stealth, we apply next-generation agentic AI to overhaul mission-critical enterprise workflows that still depend on error-prone, manual processes. Our vision is to bring these high-value operations into the modern era … event buses (Kafka, Pulsar). Wrangle large, heterogeneous data sets —model, transform, and index multi-modal, multi-terabyte enterprise datasets for advanced AI workloads Develop enterprise-level next generation AI systems with the support of our AI specialists Ship complete customer features - from architecture and code to CI/CD, infra-as-code (Terraform), rollout, and user training. … contract. Thrive in an early-stage, high-ownership environment—prototype today, deploy tomorrow, iterate next week. Bonus Points Experience deploying or consuming LLM-powered services (OpenAI, open-source models, RAG, vector stores) can be a bonus. However, we consider many great candidates without previous AI experience. What we're offering: Base salary from £115,000 - £135,000. .. plus meaningful More ❯

varied use cases. Build agentic workflows and reasoning pipelines using frameworks such as LangChain, LangGraph, CrewAI, Autogen, and LangFlow. Implement retrieval-augmented generation (RAG) pipelines using vector databases like Pinecone, FAISS, Chroma, or PostgreSQL. Fine-tune prompts to optimise performance, reliability, and alignment. Design and implement memory modules for short-term and long-term … cloud AI tools, observability platforms, and performance optimisation. This is an opportunity to work at the forefront of AI innovation, where your work will directly shape how next-generation systems interact, reason, and assist. More ❯

healthcare and cutting-edge LLM technology, shipping fast and solving meaningful problems every day. What You’ll Own Architect and develop backend microservices (Python/FastAPI) that power our RAG pipelines and analytics Build scalable infrastructure for retrieval and vector search (PGVector, Pinecone, Weaviate) Design evaluation frameworks to improve search accuracy and reduce hallucinations Deploy and manage services … LlamaIndex What We’re Looking For 5+ years building production-grade backend systems (preferably in Python) Strong background in search, recommender systems, or ML infrastructure at scale Experience with RAG architectures, embeddings, and vector search Confidence working across GCP (or AWS/Azure) and infrastructure-as-code Familiarity with observability, performance tuning, and secure data practices A growth mindset, startup More ❯

clinicians to make faster, evidence-based decisions at the bedside. We’re developing a cutting-edge platform that combines LLMs , retrieval-augmented generation (RAG) , and vector search infrastructure to deliver real-time clinical insights. As the Founding Backend Engineer , you’ll play a critical role in shaping our backend systems, architecture, and engineering culture … from the ground up. What You’ll Do Build scalable backend microservices in Python (FastAPI) to support RAG workflows and user queries Develop and optimise vector search pipelines using tools like PGVector, Pinecone, or Weaviate Design embedding orchestration and hybrid retrieval mechanisms Implement evaluation frameworks (BLEU, ROUGE, hallucination checks) to monitor answer quality Deploy production systems on GCP More ❯

infrastructure Working knowledge of Kubernetes, security best practices, and cloud platforms (AWS, GCP, or Azure) Desirable: Experience with prompt engineering, Retrieval-Augmented Generation (RAG), and graph databases Familiarity with multi-agent LLM systems and agentic platforms (e.g., AutoGen, CrewAI), and experience deploying LLM-based applications Experience with tools such as LangChain, LangSmith, or Chainlit More ❯

integrated into enterprise applications to enhance user experience, decision-making, and automation. Exposure to modern AI application patterns such as: Retrieval-Augmented Generation (RAG) for augmenting LLMs with domain-specific knowledge. Prompt engineering and fine-tuning for tailoring model behavior to business-specific contexts. Use of embedding stores and vector databases (e.g., Pinecone, Redis More ❯

and maintaining long-lived systems. Bonus Points for: Familiarity with financial services or private equity environments. Experience with Azure, Postgres, Streamlit, or similar tools. Practical experience with document-based RAG, Q&A, or fact extraction tasks. Thriving in small teams with fast feedback and iteration loops. Please, apply to learn more. More ❯

object detection (e.g. MobileNet, YOLO). Either way, we’re looking for someone who can help our app understand what the visitor is looking at – reliably and at scale. RAG Systems, Data Pipelines & Internal Agents: You'll design the data pipelines that power our AI features, including retrieval-augmented generation (RAG), internal LLM-based More ❯

time using image embeddings, similarity search (e.g. CLIP, vector search), and traditional CV approaches (e.g. YOLO, MobileNet). LLM & RAG Systems: Design and implement pipelines that support retrieval-augmented generation, internal AI tools, and scalable content delivery. Experience with vector databases, agent frameworks, or data workflows is highly relevant. Deployment & MLOps: Own model deployment More ❯

object detection (e.g. MobileNet, YOLO). Either way, we’re looking for someone who can help our app understand what the visitor is looking at – reliably and at scale. RAG Systems, Data Pipelines & Internal Agents: You'll design the data pipelines that power our AI features, including retrieval-augmented generation (RAG), internal LLM-based More ❯

Engineer The role is building AI based automation into back-office administration tasks. You'll be working with GenAI and Agentic AI, lots of AWS, NodeJS, Python, HuggingFace, LangChain, RAG techniques, interfacing with diverse data sets. The opportunity Work at the forefront of the industry. It's exciting, competitive, fast paced and challenging of course! You'll have the support 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 1-5 days a week, so we would More ❯

AI Research Engineer to help pioneer next-generation language model systems at the frontier of applied AI. In this role, you’ll help build foundational agent and RAG infrastructure, shape internal research initiatives, and accelerate the delivery of LLM-powered features to end users. You’ll collaborate cross-functionally with engineering and product teams to experiment, evaluate, and … System Development : Research, prototype, and build systems powered by large language models, focusing on reliability, efficiency, and relevance. Agent & RAG Architectures : Design and refine agentic workflows and retrieval-augmented generation pipelines that improve performance, accuracy, and cost-efficiency. Evaluation & Alignment : Develop metrics and tools to measure model performance, groundedness, and behaviour; explore fine-tuning More ❯

teams reach high-intent buyers through hyper-personalised, multi-channel campaigns. We’re building an AI-first platform from the ground up, with LLMs powering everything from content generation to strategic recommendations. We’re looking for hands-on AI Fullstack Product Engineers who’s excited to build, ship, and fully own production-grade AI features. You’ll have … ll be responsible for architecting and shipping AI-powered features using tools like PydanticAI, LangGraph, FastAPI, and OpenAI/Anthropic APIs. You’ll build and orchestrate intelligent agents and RAG pipelines, moving quickly to prototype, test, and launch in days, not months. Working closely with product, design, and go-to-market teams, you’ll build features that solve real customer More ❯

platform like Kubeflow. Demonstrated ability to transition models from prototype to production. Experience assessing various AI/ML technologies and models for fit to problem space, including scenarios where RAG is applicable. Incident response experience, and ability to work with large, noisy, and rapidly evolving threat datasets. Strong background in cloud engineering and containerisation (Docker, Kubernetes) with experience deploying AI More ❯

our agent framework Lead engineering team post-seed Shape product vision and strategy Qualifications: Insatiable desire to build the future & create a highly impactful products Deep expertise in LLMs, RAG, and production ML Experience with agent architectures and fine-tuning Strong API integration skills Builder mentality - you ship fast and iterate Previous startup experience preferred Excited to solve hard technical More ❯