1 to 25 of 135 Permanent Retrieval-Augmented Generation Jobs in the City of London

What You’ll Be Doing Designing, developing, and deploying AI and LLM-based solutions in production environments Building and maintaining retrieval-augmented generation (RAG) pipelines and integrating LLM APIs (e.g. GPT, Claude, Gemini) Developing and optimising APIs, embeddings, and backend services for AI systems Writing clean, efficient, and well-documented code in Python and … the UK (visa sponsorship not available) Nice to Have Experience with prompt engineering , fine-tuning , or AI agents Understanding of retrieval-augmented generation (RAG) systems and semantic search Exposure to enterprise AI security , multimodal AI , or reinforcement learning (RLHF) Benefits 💰 Competitive Salary: Up to £70,000 + annual performance bonus 🏡 Hybrid Working: Flexible blend More ❯

What You’ll Be Doing Designing, developing, and deploying AI and LLM-based solutions in production environments Building and maintaining retrieval-augmented generation (RAG) pipelines and integrating LLM APIs (e.g. GPT, Claude, Gemini) Developing and optimising APIs, embeddings, and backend services for AI systems Writing clean, efficient, and well-documented code in Python and … the UK (visa sponsorship not available) Nice to Have Experience with prompt engineering , fine-tuning , or AI agents Understanding of retrieval-augmented generation (RAG) systems and semantic search Exposure to enterprise AI security , multimodal AI , or reinforcement learning (RLHF) Benefits 💰 Competitive Salary: Up to £70,000 + annual performance bonus 🏡 Hybrid Working: Flexible blend 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 ❯

/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 ❯

/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 ❯

Responsibilities Architect Autonomous Agents: Design and implement robust, goal-driven AI agents using leading frameworks like LangChain, LangGraph, and the Google Agent Development Kit (ADK). Develop and Evaluate RAG Pipelines: Engineer and optimize end-to-end Retrieval-Augmented Generation (RAG) systems, including data ingestion, chunking strategies, and implementing rigorous pipeline evaluation frameworks for … Kit (ADK) LLM Expertise: Advanced Prompt Engineering and hands-on experience with model fine-tuning techniques including PEFT and QLoRA. Proven experience with models like Gemini, and Llama 3. RAG & Vector Databases: Deep expertise in RAG architecture and evaluation metrics. Proven experience with Vector Databases such as Milvus, Pinecone, or Chroma. Software & Cloud Engineering: Programming & APIs: Expert-level Python and 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 ❯

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 ❯

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 ❯

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 ❯

systems and agentic applications that drive meaningful business outcomes. The Role As our Generative AI Engineer , you’ll be at the forefront of innovation, designing and deploying next-generation AI systems using large language models (LLMs), multi-agent frameworks, and cutting-edge GenAI tooling. You’ll partner closely with stakeholders across Product, Design, and Operations to translate business … AI use cases, translate requirements, and deliver solutions that align with business goals. Develop and fine-tune LLMs and implement Retrieval-Augmented Generation (RAG) pipelines for specialized knowledge tasks. Engineer agentic workflows , including hierarchical agents, swarms, and complex coordination logic for multi-tasking and automation. Integrate GenAI tools into platforms such as WordPress and … proficient in libraries such as NumPy, Pandas, scikit-learn, PyTorch, TensorFlow. Proven experience building and scaling GenAI applications in production. Expertise in LLM architectures (OpenAI, LLaMA, Mistral, etc.) and RAG implementation. Familiarity with frameworks like LangChain, LlamaIndex, LangGraph, CrewAI, AutoGen, or similar. Experience with CI/CD for AI (GenAIOps), model monitoring, and cloud platforms: Google Vertex AI, AWS SageMaker More ❯

systems and agentic applications that drive meaningful business outcomes. The Role As our Generative AI Engineer , you’ll be at the forefront of innovation, designing and deploying next-generation AI systems using large language models (LLMs), multi-agent frameworks, and cutting-edge GenAI tooling. You’ll partner closely with stakeholders across Product, Design, and Operations to translate business … AI use cases, translate requirements, and deliver solutions that align with business goals. Develop and fine-tune LLMs and implement Retrieval-Augmented Generation (RAG) pipelines for specialized knowledge tasks. Engineer agentic workflows , including hierarchical agents, swarms, and complex coordination logic for multi-tasking and automation. Integrate GenAI tools into platforms such as WordPress and … proficient in libraries such as NumPy, Pandas, scikit-learn, PyTorch, TensorFlow. Proven experience building and scaling GenAI applications in production. Expertise in LLM architectures (OpenAI, LLaMA, Mistral, etc.) and RAG implementation. Familiarity with frameworks like LangChain, LlamaIndex, LangGraph, CrewAI, AutoGen, or similar. Experience with CI/CD for AI (GenAIOps), model monitoring, and cloud platforms: Google Vertex AI, AWS SageMaker 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 ❯

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 ❯

Model Context Protocol for managing context and tool interfaces for agents. LLM integration patterns, including prompt orchestration and tool calling. Retrieval-Augmented Generation (RAG) for dynamic context injection. Understanding of user-centric design for AI interfaces and intelligent automation. Experience with AI frameworks (PyTorch, Tensorflow, Hugging Face etc.). Preferred Skills Preferred knowledge of More ❯

Model Context Protocol for managing context and tool interfaces for agents. LLM integration patterns, including prompt orchestration and tool calling. Retrieval-Augmented Generation (RAG) for dynamic context injection. Understanding of user-centric design for AI interfaces and intelligent automation. Experience with AI frameworks (PyTorch, Tensorflow, Hugging Face etc.). Preferred Skills Preferred knowledge of More ❯

Senior Python Engineer | Build the Future of AI Infrastructure We’re looking for a Senior Python Engineer to join a fast-growing AI startup shaping the next generation of intelligent systems. You’ll play a key role in building scalable backend services, data pipelines, and infrastructure that power cutting-edge AI products used by customers worldwide. ✉️ What You … automation, and scalable engineering 💡 Nice-to-Haves Experience integrating AI models into real-world products Knowledge of LLM frameworks or retrieval-augmented generation (RAG) systems Exposure to event-driven architectures or real-time data streaming Familiarity with observability tools (Prometheus, Grafana, OpenTelemetry) 🚀 Why Join You’ll be part of a small, high-impact team 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 ❯

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 ❯

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 ❯