76 to 100 of 102 Retrieval-Augmented Generation Jobs in the City of London

from concept through to secure, scalable production deployment. You’ll work across engineering, commercial, and operational teams to develop GenAI tools including retrieval-augmented systems, intelligent assistants, and workflow automation. Your work will be central to embedding GenAI into enterprise platforms, unlocking measurable business outcomes while … ensuring maintainability, security, and compliance. Key Responsibilities Lead the design and delivery of production-grade GenAI solutions using LLMs, RAG, and vector search, optimising prompts, context, and retrieval for accuracy and performance. Architect and implement GenAI services integrated with enterprise systems and cloud platforms, ensuring scalability, security ...

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+ … 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 ...

orchestration, prompt frameworks, and evaluation loops Build multi-agent systems (planning, tool use, safety constraints, monitoring) Implement retrieval and vector-based architectures (RAG, embeddings, semantic workflows) Apply ontology and knowledge-modelling concepts for more precise reasoning Integrate APIs, tools, and enterprise systems Set the technical direction for your … practices Influence internal standards and architectures Experience Needed 6–8+ years in software, AI, or applied data engineering Strong hands-on experience with LLMs, RAG, vector stores, and embeddings Experience building or orchestrating multi-agent systems Strong Python skills and ability to ship production-grade code Experience with at least ...

OpenAI environment Build Prof. T v0.1 — our AI tutor trained on curriculum, assessments, and teaching materials Create retrieval and vector-based pipelines (RAG) for structured learning content Build internal tools for academics to upload, review, and refine materials Work with leadership to define our long-term AI roadmap … entire institution. What we’re looking for Essential Experience with Azure OpenAI, OpenAI, LangChain, or similar frameworks Strong Python engineering skills Practical experience building RAG systems, embeddings, vector stores Comfort with cloud architecture (Azure preferred) Ability to move fast, prototype, and ship Experience building MVPs, internal tools, or early-stage ...

NetMind.ai, we’re building the next-generation AI/ML platform powered by a global decentralized GPU infrastructure. Our mission is to deliver the simplest and most accessible generative AI solutions on the market and democratize access to AI technology globally. Our AI services range from inference model … within days (or hours) to support client pitches. AI Stack Integration: Utilize Claude Code Agent, Google Gemini, and OpenAI APIs to implement features like RAG, document analysis, and automated workflows. Full-Stack Lite: Use Python and Streamlit to create clean, interactive, and client-ready user interfaces. Prompt Engineering: Design ...

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 comprise of Stewardship, Best People … Langchain, AutoML, OCR, STT, feature stores, vector DB Implementation of AI/ML architectural patterns and best practices e.g. data drift detection, experimentation tracking, RAG, deployment models Building and using cloud-based solutions (networking, security, storage, monitoring, scaling, DR/HA) on one or more cloud platforms SW Engineering ...

want to shape how LLMs are deployed at scale in a real business? Have you built RAG or agentic systems that moved beyond experimentation? Are you motivated by measurable customer and commercial impact, not demos? A private-equity-backed consumer services business is scaling its Data Science & AI capability … year transformation. Following an award-winning data programme, the company now has an established AI function and is investing further in LLM-powered assistants, RAG pipelines, voice/chat intelligence and early agentic workflows across both customer-facing and internal products. This Senior AI Engineer role is a hands ...

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 … 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 ...

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/… 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 ...

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+ ...

products at scale. Sitting at the core of AI delivery, you will design, build, and operate the runtime, infrastructure, and operational layers supporting RAG pipelines, LLM orchestration, vector search, and evaluation workflows across AWS and Databricks. Working closely with senior AI engineers and product teams, you'll ensure AI systems … With further scope of responsibilities detailed below: Own and evolve the AI platform powering conversational assistants and generative AI products. Build, operate, and optimise RAG and LLM-backed services, improving latency, reliability, and cost. Design and run cloud-native AI services across AWS and Databricks, including ingestion and embedding pipelines. ...

model: London | Office-based (Mon–Fri) You’ll join an established financial services group at a pivotal moment as they build a next-generation trading and investment platform from scratch. This is a hands-on engineering role suited to someone who enjoys scaling Python services, shaping AI-native … incident response You Will Need – Strong Python engineering background with experience building scalable back-end services – Solid ML foundations with exposure to LLM integration, RAG, or prompt orchestration – Understanding of model context patterns and AI/ML tool interfaces – Experience with cloud-native delivery (AWS) and containerised systems – Ability ...

Engineer - Investment Firm - Python, LLMs, RAG, API's OB have partnered with A London based Investment Firm, who are looking to make a key technical hire of an AI Engineer, who will play a key part in how they leverage AI across the investment lifecycle. This is a highly autonomous … direction, build AI powered tools, systems and agents, and own the end-to-end delivery of AI systems. AI Engineer - Investment Firm - Python, LLMs, RAG, API's Key skills and experience: 5+ years of commercial experience Strong fundamentals with Python & SQL Experience building and deploying LLMs Familiarity with RAG & Vector ...

recommendations and assistants Integrate and deploy LLM APIs (OpenAI, Anthropic, Cohere) and open-source AI frameworks (LangChain, LlamaIndex, LangGraph) into production Design and optimise RAG pipelines using embeddings, vector databases (Pinecone, Weaviate, FAISS) and other retrieval systems Work with knowledge graphs and structured data to add richer context … real user journeys What we’re looking for Backend engineer with a strong interest in practical, product-focused AI integration Experience building semantic search, RAG pipelines or similar retrieval-heavy AI systems Hands-on with vector databases (Pinecone, Weaviate, FAISS or similar) and large, complex datasets Familiarity with ...

make a real impact. This is a hands-on role where you’ll work with cutting-edge tools and frameworks to deliver next-generation solutions. Key Responsibilities Design, fine-tune, and implement LLM-based systems for real-world applications Work with frameworks like LangChain, Hugging Face , and vector … innovation What We’re Looking For Strong experience with LLMs, NLP, and generative AI Proficiency in PHP Knowledge of prompt engineering, fine-tuning, and RAG architectures Ability to thrive in a dynamic startup setting What’s on Offer Fully remote – work from anywhere Flexible salary based on experience Rolling contract ...

extremely experienced Head of Backend and VP of engineering as well as closing working with a team of doctors helping build the next generation of AI powered clinical knowledge tools As a Senior Backend Engineer, you’ll play a key role in building and scaling the core infrastructure … build robust, scalable cloud-first backend systems. Contribute to infrastructure-as-code practices and DevOps tooling. Build and maintain LLM-powered features such as RAG pipelines for clinical information. Develop and optimise agentic document extraction workflows to process clinical PDFs, policies, and handbooks into structured formats. Collaborate closely with ...

extremely experienced Head of Backend and VP of engineering as well as closing working with a team of doctors helping build the next generation of AI powered clinical knowledge tools. If you are an experienced senior backend engineer this is one of those rare roles where your decisions … build robust, scalable cloud-first backend systems. Contribute to infrastructure-as-code practices and DevOps tooling. Build and maintain LLM-powered features such as RAG pipelines for clinical information. Develop and optimise agentic document extraction workflows to process clinical PDFs, policies, and handbooks into structured formats. Collaborate closely with ...

microservices Docker, CI/CD pipelines Cloud security & compliance (HIPAA/GDPR/NHS DTAC) Frontend React TypeScript Tailwind Component-driven development AI/RAG/Knowledge Pipelines LLM-powered features RAG pipelines (embedding models, chunking strategies, semantic search) Document extraction/processing (PDF parsing, OCR, agentic pipelines) Clinical content … code and deploy serverless services. Help architect systems that scale to hundreds of thousands of clinicians. AI & Clinical Knowledge Retrieval Build RAG workflows that safely surface medical knowledge. Develop clinical document ingestion pipelines that process PDFs, protocols, and policies. Validate AI-generated outputs with clinical context and domain ...

Founding Backend Engineer | Python, TypeScript, LLMs, RAG | Seed-Funded B2B SaaS | £100,000 - £160,000 + Early Stage Equity | London, Hybrid | Can Sponsor + Relocate The Company We’re supporting a well-funded Seed-stage B2B SaaS startup backed by top-tier European investors. They are automating a traditionally manual … expansion, deeper automation, and building intelligent systems that turn unstructured data into actionable insights at scale. About the Founding Backend Engineer | Python, TypeScript, LLMs, RAG | Seed-Funded B2B SaaS | £100,000 - £160,000 + Early Stage Equity | London, Hybrid | Can Sponsor + Relocate As the Founding AI Engineer, you will ...

Engineer - Investment Firm - Python, LLMs, RAG, API's OB have partnered with A London based Investment Firm, who are looking to make a key technical hire of an AI Engineer, who will play a key part in how they leverage AI across the investment lifecycle. This is a highly autonomous … direction, build AI powered tools, systems and agents, and own the end-to-end delivery of AI systems. AI Engineer - Investment Firm - Python, LLMs, RAG, API's Key skills and experience: 5+ years of commercial experience Strong fundamentals with Python & SQL Experience building and deploying LLMs Familiarity with RAG & Vector ...

build a predictive AI platform that surfaces opportunities before clients even know they exist? Have you led end-to-end delivery of LLM/RAG/agentic systems in production? Ready to become the technical owner of an intelligence engine at an early-stage startup? A high-growth AI/… technical scope and the chance to define the product from the ground up. The Lead AI Engineer will architect and productionise advanced LLM/RAG systems, design agentic workflows, own evaluations and guardrails, and integrate AI modules into a scalable enterprise-grade platform. You’ll collaborate with domain experts from ...

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 … 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 ...

language interfaces, and recommendations Integrate and deploy LLM APIs (OpenAI, Anthropic, Cohere) and cutting-edge open-source AI frameworks (LangChain, LlamaIndex) Design and optimise RAG pipelines leveraging embeddings and vector databases (Pinecone, Weaviate, FAISS) Develop advanced agentic workflows and harness knowledge graphs for smarter automation What we're looking … Backend engineer with deep interest in practical AI integration Comfortable building RAG pipelines and managing large, complex datasets Experience with Javascript, Node.js and vector databases Ready to dive further into agentic AI workflows and real-world product impact Hybrid: North London office, 2 days/week What's on offer ...

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 … 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 ...

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 ...