51 to 75 of 343 Retrieval-Augmented Generation Jobs in London

A leading multi-strategy investment firm is seeking a Senior AI Engineer to join a high-impact Applied AI team focused on building next-generation tools that enhance decision-making across the investment lifecycle. Operating at the intersection of finance and machine intelligence, the team is responsible for developing production-grade AI systems that empower portfolio managers, analysts … containerised environments (Docker, Kubernetes). Solid grasp of distributed systems, networking, and infrastructure-as-code practices. Familiarity with vector databases, retrieval-augmented generation (RAG), and LLM integration is a plus. Exposure to cloud platforms (AWS preferred) and messaging systems (Kafka, RabbitMQ) is advantageous. Strong communication skills and the ability to work across technical and 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 ❯

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 ❯

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 ❯

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 ❯

in AI Solution Architecture, including LLM/SLM (Large Language Models/Small Language Models) deployment, fine-tuning, inference optimization, retrieval-augmented generation (RAG), API-based AI deployment and model orchestration. Strong knowledge of Cloud AI & Hyperscalers, including AWS Bedrock, OpenAI, Google Vertex AI, Azure, hybrid and multimodal AI applications. Proficiency in Cloud Security More ❯

of OWASP Top 10 and a proactive approach to identifying and mitigating security vulnerabilities during development. Experience designing and deploying Retrieval-Augmented Generation (RAG) pipelines, working with LLM APIs (AWS Bedrock, OpenAI, Azure OpenAI), and using frameworks like LangChain or LangGraph. Strong knowledge of SDLC principles, CI/CD pipelines, and modern engineering practices. More ❯

recruiting on behalf of our global energies client for an NLP/GenAI Data Scientist who can bring a strong understanding in modern NLP, LLMs, transformer architectures, prompt-engineering, RAG, agentic architectures and evaluation methodologies. They require candidates to offer strong knowledge of Python programming for developing and debugging AI models and would expect suitable candidates to be educated to … within the GenAI/NLP team. This role will focus on the application and development of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) systems, and domain-specific GenAI solutions to support key internal use cases and products. Responsibilities In this role you will: Design, implement and maintain scalable NLP and GenAI pipelines (including … to date with state-of-the-art research in the space of LLMs/NLP, proposing new ideas and methodologies that unlock business value. Contribute to the development of RAG systems and retrieval pipelines, including chunking, embedding, re-ranking, and evaluation. Participate in experiments, including designing experimental details, writing reusable code, running evaluations, and organising results. Collaborate with More ❯

recruiting on behalf of our global energies client for an NLP/GenAI Data Scientist who can bring a strong understanding in modern NLP, LLMs, transformer architectures, prompt-engineering, RAG, agentic architectures and evaluation methodologies. They require candidates to offer strong knowledge of Python programming for developing and debugging AI models and would expect suitable candidates to be educated to … within the GenAI/NLP team. This role will focus on the application and development of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) systems, and domain-specific GenAI solutions to support key internal use cases and products. Responsibilities In this role you will: Design, implement and maintain scalable NLP and GenAI pipelines (including … to date with state-of-the-art research in the space of LLMs/NLP, proposing new ideas and methodologies that unlock business value. Contribute to the development of RAG systems and retrieval pipelines, including chunking, embedding, re-ranking, and evaluation. Participate in experiments, including designing experimental details, writing reusable code, running evaluations, and organising results. Collaborate with More ❯

Meet Eloquent AI (YC X25) At Eloquent AI, we’re building the next generation of AI Operators—multimodal, autonomous systems that execute complex workflows across fragmented tools with human-level precision. Our technology goes far beyond chat: it sees, reads, clicks, types, and makes decisions—transforming how work gets done in regulated, high-stakes environments. We’re already … powered UIs to ensure seamless and intuitive user experiences. Work closely with AI researchers and ML engineers to integrate LLMs, Retrieval-Augmented Generation (RAG), and automation into production-ready applications. Ship robust, minimal-dependency code that performs efficiently in enterprise environments. Continuously iterate and refine AI-driven products, balancing user needs with technical feasibility. More ❯

AI technology. This is a hands-on, customer-facing role responsible for designing and delivering enterprise-grade AI solutions leveraging retrieval-augmented generation (RAG), fine-tuning, and inference deployment across 's sovereign AI Mesh. You'll blend deep technical expertise with commercial acumen — engaging customers, architecting solutions, and ensuring seamless deployment of AI systems … that drive real-world transformation. Key Responsibilities: Customer Solution Design Engage with enterprise customers to understand business goals and map them to AI workflows. Architect RAG pipelines, fine-tuning strategies, and inference endpoint deployments using our key products Collaborate with GTM and sales teams during pre-sales cycles, workshops, and proof-of-concepts. AI and ML Engineering Implement, optimise, and … foundation models for domain-specific use cases. Package and deploy models into scalable inference endpoints (REST/gRPC APIs, containerised or GPU-accelerated environments). Data and Knowledge Integration (RAG) Build pipelines that connect structured and unstructured enterprise data into vector databases and embedding models . Design semantic search and retrieval strategies to maximise response accuracy and relevance. More ❯

opportunities and deliver scalable AI workflows. You will design prompts, integrate Large Language Models (LLMs) with enterprise systems, and implement Retrieval-Augmented Generation (RAG) pipelines. You will also advise on responsible AI practices, ensuring compliance and governance. Key responsibilities include: Working closely with Heads of departments to scope out projects Designing and optimizing prompts … for reliable LLM outputs Building and deploying AI-driven workflows connecting LLMs to applications, APIs, and automation tools Implementing RAG pipelines to link enterprise data with LLMs Prototyping and iterating to demonstrate business value quickly Advising on best practices for responsible AI adoption Candidate Profile Strong experience with LLMs (e.g. GPT, Claude, LLaMA) and prompt engineering Hands-on integration with … LlamaIndex, or similar Proficiency in Python or JavaScript for building prototypes and integrations Familiarity with automation platforms (UiPath, Power Automate, Zapier, n8n) Knowledge of vector databases and embeddings for RAG pipelines Excellent communication skills to translate business problems into technical solutions Experience working in agile or fast-paced environments 3+ years experience Location & Working Model Based in London Five days More ❯

opportunities and deliver scalable AI workflows. You will design prompts, integrate Large Language Models (LLMs) with enterprise systems, and implement Retrieval-Augmented Generation (RAG) pipelines. You will also advise on responsible AI practices, ensuring compliance and governance. Key responsibilities include: Working closely with Heads of departments to scope out projects Designing and optimizing prompts … for reliable LLM outputs Building and deploying AI-driven workflows connecting LLMs to applications, APIs, and automation tools Implementing RAG pipelines to link enterprise data with LLMs Prototyping and iterating to demonstrate business value quickly Advising on best practices for responsible AI adoption Candidate Profile Strong experience with LLMs (e.g. GPT, Claude, LLaMA) and prompt engineering Hands-on integration with … LlamaIndex, or similar Proficiency in Python or JavaScript for building prototypes and integrations Familiarity with automation platforms (UiPath, Power Automate, Zapier, n8n) Knowledge of vector databases and embeddings for RAG pipelines Excellent communication skills to translate business problems into technical solutions Experience working in agile or fast-paced environments 3+ years experience Location & Working Model Based in London Five days More ❯

opportunities and deliver scalable AI workflows. You will design prompts, integrate Large Language Models (LLMs) with enterprise systems, and implement Retrieval-Augmented Generation (RAG) pipelines. You will also advise on responsible AI practices, ensuring compliance and governance. Key responsibilities include: Working closely with Heads of departments to scope out projects Designing and optimizing prompts … for reliable LLM outputs Building and deploying AI-driven workflows connecting LLMs to applications, APIs, and automation tools Implementing RAG pipelines to link enterprise data with LLMs Prototyping and iterating to demonstrate business value quickly Advising on best practices for responsible AI adoption Candidate Profile Strong experience with LLMs (e.g. GPT, Claude, LLaMA) and prompt engineering Hands-on integration with … LlamaIndex, or similar Proficiency in Python or JavaScript for building prototypes and integrations Familiarity with automation platforms (UiPath, Power Automate, Zapier, n8n) Knowledge of vector databases and embeddings for RAG pipelines Excellent communication skills to translate business problems into technical solutions Experience working in agile or fast-paced environments 3+ years experience Location & Working Model Based in London Five days More ❯

opportunities and deliver scalable AI workflows. You will design prompts, integrate Large Language Models (LLMs) with enterprise systems, and implement Retrieval-Augmented Generation (RAG) pipelines. You will also advise on responsible AI practices, ensuring compliance and governance. Key responsibilities include: Working closely with Heads of departments to scope out projects Designing and optimizing prompts … for reliable LLM outputs Building and deploying AI-driven workflows connecting LLMs to applications, APIs, and automation tools Implementing RAG pipelines to link enterprise data with LLMs Prototyping and iterating to demonstrate business value quickly Advising on best practices for responsible AI adoption Candidate Profile Strong experience with LLMs (e.g. GPT, Claude, LLaMA) and prompt engineering Hands-on integration with … LlamaIndex, or similar Proficiency in Python or JavaScript for building prototypes and integrations Familiarity with automation platforms (UiPath, Power Automate, Zapier, n8n) Knowledge of vector databases and embeddings for RAG pipelines Excellent communication skills to translate business problems into technical solutions Experience working in agile or fast-paced environments 3+ years experience Location & Working Model Based in London Five days More ❯

in Python and modern AI frameworks (e.g. PyTorch, TensorFlow, LangChain, Hugging Face). Familiarity with LLM fine-tuning, reasoning, or retrieval-augmented generation (RAG). Desirable: Experience with AI orchestration, workflow automation, or public-sector technology projects. Knowledge of frameworks such as ReAct, AutoGen, CrewAI, or BabyAGI. Understanding of ethical AI and governance standards. More ❯

in Python and modern AI frameworks (e.g. PyTorch, TensorFlow, LangChain, Hugging Face). Familiarity with LLM fine-tuning, reasoning, or retrieval-augmented generation (RAG). Desirable: Experience with AI orchestration, workflow automation, or public-sector technology projects. Knowledge of frameworks such as ReAct, AutoGen, CrewAI, or BabyAGI. Understanding of ethical AI and governance standards. More ❯

in Python and modern AI frameworks (e.g. PyTorch, TensorFlow, LangChain, Hugging Face). Familiarity with LLM fine-tuning, reasoning, or retrieval-augmented generation (RAG). Desirable: Experience with AI orchestration, workflow automation, or public-sector technology projects. Knowledge of frameworks such as ReAct, AutoGen, CrewAI, or BabyAGI. Understanding of ethical AI and governance standards. More ❯

in Python and modern AI frameworks (e.g. PyTorch, TensorFlow, LangChain, Hugging Face). Familiarity with LLM fine-tuning, reasoning, or retrieval-augmented generation (RAG). Desirable: Experience with AI orchestration, workflow automation, or public-sector technology projects. Knowledge of frameworks such as ReAct, AutoGen, CrewAI, or BabyAGI. Understanding of ethical AI and governance standards. 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 ❯

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 ❯

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 ❯

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 ❯

delivers. You'll own projects end-to-end - designing data pipelines, integrating with vector databases, and deploying intelligent features using Retrieval-Augmented Generation (RAG) and fine-tuned LLMs. What You'll Bring: Deep hands-on experience with web scraping tools and frameworks (Scrapy, Playwright, Puppeteer, etc.) 5+ years in data engineering, ML engineering, or … Experience managing NoSQL and vector databases at scale (MongoDB, Weaviate, Pinecone, etc.) Solid understanding of modern data pipeline tools (Airflow, Prefect, Dagster) Practical experience with LLM development, embeddings, and RAG architectures Familiarity with distributed systems and cloud platforms (AWS, GCP, or Azure) Self-motivated and capable of independently delivering production-grade systems Why You Should Apply: Own the backbone of More ❯

Integration & Deployment Model Deployment: Triton Inference Server, Hugging Face Inference Endpoints LangChain, LlamaIndex for building LLM-powered applications Vector Databases: 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 ❯