76 to 100 of 241 Retrieval-Augmented Generation Jobs in England

skills Nice to have Understanding and/or hands-on experience of Reinforcement Learning theories, frameworks, and algorithms Experience with cloud infrastructure Experience with Large Language Models (fine tuning, RAG, agents) Experience with graph technology and/or algorithms, understanding of NLP algorithms Our technology stack Python and associated ML/DS libraries (scikit-learn, NumPy, LightGBM, Pandas, TensorFlow, etc More ❯

AI/ML services. Strong foundation in software engineering principles for building scalable, maintainable, and production-ready AI systems. Experience in designing and implementing enterprise-grade AI solutions, including RAG-based solutions with LLMs and vector databases (e.g., Pinecone, Weaviate, FAISS). Proven experience in full stack development and AI/ML system implementation within enterprise environments. Strong grasp of More ❯

AI/ML services. Strong foundation in software engineering principles for building scalable, maintainable, and production-ready AI systems. Experience in designing and implementing enterprise-grade AI solutions, including RAG-based solutions with LLMs and vector databases (e.g., Pinecone, Weaviate, FAISS). Proven experience in full stack development and AI/ML system implementation within enterprise environments. Strong grasp of More ❯

Hands-on exposure to AWS (e.g. EC2, S3, IAM; bonus points for Lambda, ECS/EKS, SageMaker, or Bedrock) Familiarity with LLM frameworks and tooling (e.g. LangChain, vector databases, RAG pipelines) is highly advantageous Genuine interest in AI compliance, governance, and emerging regulation (e.g. EU AI Act, model risk, responsible AI) Strong problem-solving mindset with a passion for building More ❯

Hands-on exposure to AWS (e.g. EC2, S3, IAM; bonus points for Lambda, ECS/EKS, SageMaker, or Bedrock) Familiarity with LLM frameworks and tooling (e.g. LangChain, vector databases, RAG pipelines) is highly advantageous Genuine interest in AI compliance, governance, and emerging regulation (e.g. EU AI Act, model risk, responsible AI) Strong problem-solving mindset with a passion for building More ❯

Mastery: Strong skills in Python, JavaScript, or similar languages. LLM & Agent Expertise: Hands-on experience with LLMs (OpenAI, Anthropic, Mistral) and agent frameworks. Advanced Tools: Familiarity with vector databases, RAG pipelines, prompt engineering, REST APIs, cloud deployment, and containerization (Docker/Kubernetes). Domain Knowledge: Experience in one or more business domains such as finance, housing, operations, or customer service. More ❯

across multiple industries. What You’ll Be Doing: Designing, developing, and deploying machine learning and AI models Designing, developing, and deploying LLM applications (e.g. GPT, LLaMA, Claude) integrated with RAG pipelines Implementing end-to-end workflows: from data acquisition, cleaning, and feature engineering to model training, deployment, and monitoring Building scalable pipelines and APIs for AI services in cloud environments More ❯

across multiple industries. What You’ll Be Doing: Designing, developing, and deploying machine learning and AI models Designing, developing, and deploying LLM applications (e.g. GPT, LLaMA, Claude) integrated with RAG pipelines Implementing end-to-end workflows: from data acquisition, cleaning, and feature engineering to model training, deployment, and monitoring Building scalable pipelines and APIs for AI services in cloud environments More ❯

best practices across teams AI/ML Expertise Strong understanding of machine learning frameworks (TensorFlow, PyTorch, Scikit-learn) Experience with LLM integration (OpenAI, Anthropic, open-source models) Knowledge of RAG architectures, prompt engineering, and vector databases (Pinecone, Weaviate) Experience with MLOps tools and monitoring model performance in production Automation Architecture Deep knowledge of automation tools including GitHub Actions, Terraform, and More ❯

best practices across teams AI/ML Expertise Strong understanding of machine learning frameworks (TensorFlow, PyTorch, Scikit-learn) Experience with LLM integration (OpenAI, Anthropic, open-source models) Knowledge of RAG architectures, prompt engineering, and vector databases (Pinecone, Weaviate) Experience with MLOps tools and monitoring model performance in production Automation Architecture Deep knowledge of automation tools including GitHub Actions, Terraform, and More ❯

AI Engineers x 2 + fully remote contract + initially 6 months + £500 to £525 per day - Inside IR35 Key Skills: + Design of Gen AI Models + RAG + AI/ML Pipelines The Role: + Design, prototype, and deploy Generative AI models (LLMs, Transformers, Diffusion models) for enterprise use … cases. + Build and fine-tune LLM-based applications (chatbots, summarization, document Q&A, report generation, code assistants, etc.). + Apply prompt engineering, RAG (Retrieval-Augmented Generation), and context-aware pipelines to ensure accuracy and relevance. + Integrate AI models with enterprise systems, APIs, and data stores using Python, Java, or … . 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 (Pinecone, Weaviate, FAISS, Chroma) for semantic retrieval use cases. Partner with business stakeholders to identify and shape AI use cases. Contribute to More ❯

AI Engineers x 2 + fully remote contract + initially 6 months + £500 to £530 per day - Inside IR35 Key Skills: + Design of Gen AI Models + RAG + AI/ML Pipelines The Role: + Design, prototype, and deploy Generative AI models (LLMs, Transformers, Diffusion models) for enterprise use … cases. + Build and fine-tune LLM-based applications (chatbots, summarization, document Q&A, report generation, code assistants, etc.). + Apply prompt engineering, RAG (Retrieval-Augmented Generation), and context-aware pipelines to ensure accuracy and relevance. + Integrate AI models with enterprise systems, APIs, and data stores using Python, Java, or … . 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 (Pinecone, Weaviate, FAISS, Chroma) for semantic retrieval use cases. Partner with business stakeholders to identify and shape AI use cases. Contribute to More ❯

on experience deploying ML models in production environments. Excellent programming skills in Python and familiarity with ML/DL libraries (TensorFlow, PyTorch, scikit-learn, Pandas). Practical experience with RAG or agentic AI frameworks (LangChain, LlamaIndex). Experience working with LLM APIs (e.g. Hugging Face, OpenAI). Exposure to conversational AI platforms (Dialogflow, Lex, Rasa, etc.). Ability to work More ❯

on experience deploying ML models in production environments. Excellent programming skills in Python and familiarity with ML/DL libraries (TensorFlow, PyTorch, scikit-learn, Pandas). Practical experience with RAG or agentic AI frameworks (LangChain, LlamaIndex). Experience working with LLM APIs (e.g. Hugging Face, OpenAI). Exposure to conversational AI platforms (Dialogflow, Lex, Rasa, etc.). Ability to work More ❯

stakeholders Empower teams to scale AI across the organisation Maintain technology RADAR and manage AI governance and risk Required Skills & Experience Production experience with Generative AI, LLMs, NLP, and RAG systems Strong knowledge of Knowledge Graphs and semantic technologies Proficient in Python, SQL, PyTorch/TensorFlow Experience with DevOps, CI/CD, DevSecOps and secure ML pipelines. Proven experience in More ❯

stakeholders Empower teams to scale AI across the organisation Maintain technology RADAR and manage AI governance and risk Required Skills & Experience Production experience with Generative AI, LLMs, NLP, and RAG systems Strong knowledge of Knowledge Graphs and semantic technologies Proficient in Python, SQL, PyTorch/TensorFlow Experience with DevOps, CI/CD, DevSecOps and secure ML pipelines. Proven experience in More ❯

Bachelor’s or Master’s degree in a STEM field (Computer Science, Data Science, Engineering, Mathematics, etc.). Strong proficiency in Python Hands-on experience with LLMs , prompt design , RAG architectures , or fine-tuning foundation models . Understanding of MLOps practices (e.g., Docker, FastAPI, MLflow, cloud-based model deployment). Strong grasp of NLP techniques and evaluation methods for generative More ❯

enterprise use cases. Build and fine-tune LLM-based applications (chatbots, summarization, document Q&A, report generation, code assistants, etc.). Apply prompt engineering, RAG (Retrieval-Augmented Generation), and context-aware pipelines to ensure accuracy and relevance. Integrate AI models with enterprise systems, APIs, and data stores using Python, Java, or Node.js … . 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 (Pinecone, Weaviate, FAISS, Chroma) for semantic retrieval use cases. Partner with business stakeholders to identify and shape AI use cases. Contribute to More ❯

leading a small, high-performing team. What You’ll Do Build and maintain data pipelines and cloud infrastructure (AWS/GCP). Develop AI-driven applications including LLM tools, RAG pipelines, and automation solutions. Lead on client communication , project delivery, and stakeholder alignment. Collaborate with Data Science and Analytics teams to improve data access, automation, and governance. Drive innovation through More ❯

Solid understanding of REST APIs, cloud platforms (Azure/AWS/GCP), and agile delivery A passion for clean code, scalability, and technical excellence Nice to Have Experience with RAG systems, multi-agent AI, or Langfuse/LiteLLM Python proficiency for AI/ML integration Familiarity with MLOps and cost optimisation for AI systems Background in B2B SaaS or financial More ❯

and TypeScript, Familiarity with Agentic Frameworks (e.g. LangChain, LangGraph) Proven experience building full stack applications using Git, SQL databases and modern deployment practices Understanding of LLMs, embedding models and RAG systems Solid software engineering fundamentals and secure coding practices Strong problem-solving, communication and teamwork skills At least 3 years hands-on engineering experience in project delivery environments Attention to More ❯

principles and software testing practices. Experience delivering customer-facing products and supporting the full development lifecycle. Excellent communication, stakeholder engagement, and advisory skills. Desirable Experience with Generative AI, LLMs, RAG, LangChain, or Semantic Kernel. Familiarity with Microsoft Power Platform. Front-end experience with React, Angular, Vue.js, Flutter, or Progressive Web Apps. Exposure to edge computing, VR/AR, or robotics. More ❯

principles and software testing practices. Experience delivering customer-facing products and supporting the full development lifecycle. Excellent communication, stakeholder engagement, and advisory skills. Desirable Experience with Generative AI, LLMs, RAG, LangChain, or Semantic Kernel. Familiarity with Microsoft Power Platform. Front-end experience with React, Angular, Vue.js, Flutter, or Progressive Web Apps. Exposure to edge computing, VR/AR, or robotics. More ❯

and Generative AI R&D. Strong understanding of machine learning, data modelling , and natural language processing (NLP) techniques. Experience with fine-tuning , prompt orchestration , and retrieval methods (RAG, knowledge graphs). Familiarity with frameworks such as LangGraph , Azure AI Foundry Agents , and Semantic Kernel Agents . Good understanding of software engineering best practices , including version control, testing, and More ❯

and defining success metrics. Contribute reusable components and frameworks for faster deployments. Qualifications 3+ years building production-grade AI/data solutions. Hands-on GenAI experience (LLMs, prompt engineering, RAG, vector databases, agentic frameworks like LangChain, LlamaIndex, DSPy). Strong Python proficiency with a GitHub portfolio showcasing MLE or GenAI projects. Expertise in Machine Learning Engineering (model development, deployment, monitoring More ❯