51 to 71 of 71 Retrieval-Augmented Generation Jobs in Central London

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 ❯

researchers, engineers, and portfolio specialists, solving complex real-world problems. Competitive compensation with a performance-linked bonus. What You’ll Do Take ownership of designing and building next-generation AI systems that make complex financial data instantly accessible and actionable. Partner closely with data scientists, ML researchers, and frontend engineers to turn research concepts into robust, scalable production … deployment practices using Docker, Kubernetes, and CI/CD pipelines. Comfort working in cloud-based environments (AWS preferred), including data connectivity and infrastructure-as-code principles. Experience integrating LLMs, RAG pipelines, or vector databases into production workflows is a major plus. A strong communicator who can translate technical complexity into business value and thrives in collaborative, cross-functional environments. Passion 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 ❯

feedback loops. 25% Architect & scale Own reliability, latency, and cost. Design online/offline eval harnesses, canaries, and SLAs; operate GPUs/accelerators where needed. Stand up and harden RAG pipelines (indexing, retrieval policies, grounding, guardrails) and agent frameworks. Take basic infra ownership on GCP (or AWS/Azure): networking, autoscaling, CI/CD, IaC, observability, and cost … building production ML/back‐end systems; 2+ years leading while coding. Expert Python ; strong back‐end chops (e.g., FastAPI, gRPC, Postgres, pub/sub/streams). Agents & RAG: Fluency with at least one agent framework ( ADK preferred ). Proven track record shipping AI agents and building RAG pipelines. LLM + DS depth: Prompting/tooling, retrieval More ❯

Senior AI Engineer Python, PyTorch, LLMs, RAG, Knowledge Graphs I’m working with one of the most ambitious AI startups in London, and they’re quickly becoming one of the leaders in the intersection of Generative AI and Finance💸 The vision? Replace clunky PDFs, fragmented data, and manual analysis with multi-agent AI systems that read, reason, and cite financial … insights. Why this role stands out: 🤖 Work directly on multi-agent LLM systems powering deep financial research 🧠 Tackle complex AI problems like knowledge graphs, RAG pipelines, citations, and personalisation 🚀 Join a tight-knit 3-person AI team at a company 8 months ahead of roadmap What they’re looking for: 3–6 years’ experience writing production-grade code Strong foundation … in machine learning (PyTorch/JAX/TensorFlow) Deep understanding of LLMs and RAG, not just LangChain wrappers Bonus: Research internships, papers, or early-stage AI product builds Someone who wants to build real things, not just publish Other details: 💸 Up to £140k + strong equity 📍2–3 days/week in the London office 📅 Process can wrap in under More ❯

Python preferred; additional exposure to JavaScript/TypeScript or Go beneficial). Strong background in AI systems, cloud-native architectures, and modern ML tooling (LangChain, OpenAI API, vector stores, RAG pipelines). Experience designing distributed, high-availability platforms deployed on AWS or similar cloud environments. Excellent communication and stakeholder management skills, with the ability to translate technical direction into business … to the property, SaaS, or workflow automation space is advantageous. Summary A rare opportunity for a technically strong, execution-focused engineering leader to shape and scale a next-generation AI automation platform from the ground up — with full ownership of the engineering strategy, team, and architecture. More ❯

ll Do Architecture & Development : Design and implement the cognitive systems that give AI agents consistent personalities, memory, and reasoning capabilities, using advanced LLM techniques like chain-of-thought prompting, RAG systems, and agentic tool use. Modeling & Experimentation : Design and run systematic experiments to evaluate agent behavior, test hypotheses about behavioral patterns, and iterate on model architectures based on empirical results … principles Experience working in fast-paced environments where requirements evolve rapidly Technical Skills LLM & Agent Development : Hands-on experience building applications with large language models, implementing advanced prompting techniques, RAG systems, and agentic workflows Backend Engineering : Proficient in Python and backend frameworks (e.g. FastAPI, Django, Flask); understanding of distributed systems and scalable architectures AI/ML Frameworks : Experience with PyTorch More ❯

Are you an AI engineer with strong software engineering fundamentals? Have you deployed AI models into production—not just trained them? Want to work on agentic systems, RAG pipelines, and LLM-powered tools? We're working with a high-growth GenAI consultancy building real-world, production-grade AI applications. With a team of 50+, the company partners with global clients … deployment, collaborating with cross-functional teams and mentoring other engineers. Key Responsibilities Build and deploy scalable AI tools using Python and LLM APIs Design end-to-end agentic and RAG-based solutions for enterprise use Own delivery from proof-of-concept through to production Work with cloud-native architectures (e.g. AWS Lambda, Step Functions) Lead and guide junior engineers within More ❯

extraction, sentiment analysis, and qualitative insights Define reporting standards: Set quality bars for what makes a great dashboard (agent performance, app store trends, CSAT drivers, topic evolution, etc.) Optimize RAG pipelines: Design retrieval strategies and grounding approaches for report generation to ensure factual, relevant outputs Customer-Facing Analytics & Enablement (30%) Deliver bespoke insights: Partner with key … for accuracy and relevance, ensure quality before launch Operations & Optimization (10%) Monitor report health: Track delivery, engagement, and quality metrics; debug when outputs degrade Performance tuning: Optimize report generation costs and latency; balance API usage with quality Document everything: Maintain clear documentation for prompts, templates, and best practices What Makes You a Great FitTechnical Foundation 5+ years in … building customer insights, product analytics, or data-driven reporting AI-native experience: Hands-on work building reports or products using LLMs—prompt engineering, structured output generation, embeddings, RAG, summarization pipelines Python proficiency: Comfortable with pandas, OpenAI library, API integrations, and data manipulation for prototyping and analysis SQL fluency: Can write complex queries and understand data modeling Analytics tools More ❯

operate as "Forward Deployed Engineers," bridging the gap between our clients' most ambitious goals and our core engineering teams. Our mission is to architect and deliver the next generation of commerce. We work on innovative projects from strategy through implementation, using the latest technologies – with a particular focus on Generative AI – to help our clients achieve market leadership. … client-facing engineering roles. Full Stack development expertise (Python, JavaScript). Hands-on cloud experience (Azure or GCP). Integration with Generative AI services and AI/ML patterns (RAG, MLOps). Strong database management skills. Nice-to-have: Cloud certifications (Azure/GCP). Experience with vector databases (Pinecone, Weaviate). Retail or CPG industry exposure. Why Accenture? Competitive … technology and operations, with digital capabilities across all of these services. With our thought leadership and culture of innovation, we apply industry expertise, diverse skill sets, and next-generation technology to each business challenge. We believe in inclusion and diversity and supporting the whole person. Our core values include Stewardship, Best People, Client Value Creation, One Global Network More ❯

Software Engineer who can stand on their own two feet and deliver complex solutions with confidence. You’ll be joining a small, sharp team building AI-driven products — think RAG pipelines , GenAI integrations , and LLM-powered features that actually make an impact. This isn’t a role where you hide behind Jira tickets. You’ll be hands-on , trusted to … make big decisions, and involved across the full stack. You’ll be working with: 🧠 Python (Django) ⚡ JavaScript (React or Vue) ☁️ AWS 🔁 CI/CD 💾 SQL 🤖 GenAI, RAG pipelines & API integrations What we’re looking for: Someone who can deliver end-to-end technical solutions independently Excellent communicator (small team, no silos) Real-world experience with AI/GenAI projects A More ❯

doing: • Owning the design and build of LLM-based systems end-to-end • Fine-tuning and adapting models (LoRA, instruction tuning, PEFT etc) rather than just prompt-engineering • Building RAG workflows, embedding strategies, memory layers and domain grounding • Working out how to measure output quality, reduce hallucination risk and improve robustness • Optimising inference performance (quantisation, distillation, pruning, batching, caching) • Deploying … prototyping/notebooks • Strong Python and experience with PyTorch/Hugging Face/similar tooling • Experience deploying models into production (not just training them) • Familiarity with vector stores and RAG patterns • Comfortable operating in a hybrid environment and speaking with stakeholders where required • Someone who enjoys solving real problems end-to-end: from understanding the domain → designing the approach → shipping More ❯

Lead | £100,000 + 20% Bonus | Hybrid - 2 Days per Week in London 💰 £100,000 + 20% Bonus 📍 London - 2 days a week in office 🛠️ AWS, Python, Terraform, Kubernetes, RAG/GenAI interest Here at Burns Sheehan, we're exclusively partnered with a leading UK fintech that's transforming small business lending - processing billions in decisions annually. They've built … CloudFormation) and cloud-native development. Python scripting proficiency at platform engineer level but an understanding of software development lifecycle and production system ownership. Interest or experience in GenAI infrastructure - RAG, vector databases, LLM platforms, or agent frameworks. Strong communication skills and ability to translate technical decisions into business outcomes. This role is NOT an ML Engineering position - you won't 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 ❯

Do you want to build the infrastructure powering the next generation of AI agents? Have you scaled backend systems that drive automation at speed? Ready to join a profitable AI startup transforming how industries deploy technology? A profitable, fast-growing AI company is building the “roads” for AI agents — providing the infrastructure that lets businesses deploy and integrate …/LLM components into production systems. Role Breakdown: 50% Backend Engineering: FastAPI, Flask, Node.js, CI/CD 30% Data Engineering: ETL, DBT, Airflow 20% AI/LLM Integration: LangChain, RAG pipelines, orchestration Key Responsibilities: Design and build backend services to support AI agent deployment Develop scalable data pipelines and integration layers Implement AI/LLM-powered features with LangChain and … of Front-End, and adoption engineers Reporting Line: CTO Visa: Cannot sponsor Ideal Profile: 5–8 years’ experience in backend or data engineering (Python) Exposure to AI/LLMs, RAG, or personal AI side projects Experience in startups or scale-ups where you’ve helped build or grow systems Solid education and strong coding fundamentals Comfortable working in a small More ❯

We are looking for Software Engineers to join our partner company! You will play a key role in building next-generation infrastructure and AI systems that accelerate innovation across industries such as aerospace, healthcare, and energy — fields still limited by outdated, manual processes. This is not a traditional engineering role. It’s a chance to join an early … technologies. Design and ship backend systems from day one — scalable architectures, APIs, and AI-powered tools. Build and deploy LLM-based applications using frameworks such as LangChain, semantic search, RAG, and fine-tuning (SFT/RL). Develop backend logic and data pipelines to power intelligent automation and real-time processing. Manage infrastructure: relational and graph databases, containerization, CI/… Backend, or AI Engineer. Proven track record of building and shipping production-ready systems. Hands-on experience with distributed systems, APIs, databases, and cloud infrastructure. Familiarity with LLMs, LangChain, RAG, or fine-tuning techniques (SFT/RL). Strong problem-solving and debugging abilities with a proactive, builder’s mindset. Comfortable in a fast-paced, early-stage environment where you 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 ❯

LLM tooling and libraries (e.g., Hugging Face Transformers/PEFT, tokenisers, spaCy or similar) Experience shipping NLP systems: prompt engineering, fine-tuning (e.g., LoRA/PEFT), vector search, and RAG-based services Great knowledge of NLP algorithms: tokenisation, embeddings, attention, language modelling, text classification/generation, and information retrieval Desirable Skills 👌 Can speak, or learning to More ❯

translate business needs into scalable architecture on the Microsoft platform. The ideal candidate will be a thought leader, trusted advisor, and hands-on architect capable of shaping next-generation data platforms and driving adoption across stakeholder groups. Lead end-to-end architecture design and implementation for Microsoft Fabric across enterprise data programs. Collaborate with business and technical stakeholders … Lakehouse architecture, Delta/Parquet formats, and data governance tools. Experience integrating Fabric with Azure-native services (e.g., Azure AI Foundry, Purview, Entra ID). Familiarity with Generative AI, RAG-based architecture, and Fabric Notebooks is a plus. About Capgemini Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and More ❯