26 to 50 of 928 LLM Jobs in the UK

implement advanced AI solutions spanning traditional machine learning, Generative AI, and Agentic AI paradigms. This includes leading hands-on development of models and RAG pipelines to support enterprise-grade LLM applications. MLOps and LLMOps : Build and maintain scalable MLOps and LLMOps pipelines, enabling automation of model training, testing, deployment, and monitoring. Enable reproducibility, traceability, and performance optimization across all AI … RAG. Strong grasp of prompt engineering techniques, including zero-shot and few-shot prompting strategies with ability to enhance the performance of LLMs and agentic AI systems. Familiarity of LLM model evaluation techniques such as ROUGE, LLM-as-a-Judge and BERT. Deep knowledge of cloud-native AI platforms and tools, particularly Databricks, Azure, AWS, Docker. Awareness of regulatory and … compliance considerations in AI, especially within financial services and other regulated industries. Experience Hands-on experience with leading ML frameworks (e.g., PyTorch, TensorFlow) and LLM libraries (e.g., Hugging Face, LangChain/LangGraph, LlamaIndex). Practical experience implementing CI/CD pipelines using tools like GitHub Actions or Jenkins, and managing MLOps and LLMOps with MosiacAI, MLflow, Sagemaker or similar platforms. More ❯

of API design, data modelling, and microservice architecture. Excellent communication skills, with 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 More ❯

of API design, data modelling, and microservice architecture. Excellent communication skills, with 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 More ❯

Key Responsibilities Design, build, and deploy Python-based AI and ML applications . Research and develop new Generative AI techniques, including fine-tuning and prompt engineering. Develop and maintain LLM-assisted frameworks and automation pipelines. Write clean, maintainable, and well-documented code for scalable deployment. Build and implement testing and evaluation frameworks for AI models. Collaborate with data scientists, software More ❯

and retraining pipelines. Exposure to feature stores, registries, and experimentation frameworks. Familiarity with business-driven metrics and experience balancing ML performance with commercial goals. Experience with generative AI and LLM frameworks for fine tuning, evaluation, deployment and serving desirable. About Rightmove Our vision is to give everyone the belief they can make their move. We aim to make moving simpler More ❯

Deployment: Design, prototype, and deploy state-of-the-art Generative AI models (e.g., LLMs, Transformers, Diffusion models ) tailored for enterprise use cases. Application Development: Build, fine-tune, and deploy LLM-based applications such as advanced chatbots, summarization tools, document Q&A systems, and code assistants. Advanced Techniques: Apply sophisticated techniques like Prompt Engineering , Retrieval-Augmented Generation (RAG) , and context-aware More ❯

Deployment: Design, prototype, and deploy state-of-the-art Generative AI models (e.g., LLMs, Transformers, Diffusion models ) tailored for enterprise use cases. Application Development: Build, fine-tune, and deploy LLM-based applications such as advanced chatbots, summarization tools, document Q&A systems, and code assistants. Advanced Techniques: Apply sophisticated techniques like Prompt Engineering , Retrieval-Augmented Generation (RAG) , and context-aware More ❯

and drive student success. WHAT YOU WILL BE DOING: Design and develop AI-driven systems and tools that enhance the learning experience for students and educators. Build and deploy LLM-based solutions, integrating frameworks such as LangChain, AutoGen, and RAG pipelines. Develop and optimise AI agents capable of reasoning, planning, and automating complex learning or data workflows. Collaborate with cross More ❯

tracking, deployment, monitoring). Great communication: ideally, you’ve mentored, taught, or led internal training before. Curious, adaptive mindset; you love learning as much as teaching. Bonus points for: LLM/GenAI experience (RAG, vector DBs, evaluation, optimisation). Experience in apprenticeship/bootcamp teaching or internal enablement. Content creation, open-source work, or public speaking. Success looks like: Learners More ❯

gRPC) and working with 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 non-technical More ❯

technical debt management Ability to establish coding standards and 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 More ❯

technical debt management Ability to establish coding standards and 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 More ❯

Foundry (full training provided). Familiarity with AI/ML Ops pipelines , real-time analytics, or edge deployments. Big Data stack knowledge (e.g., Hadoop, Spark, Kafka). GenAI/LLM experience (e.g., AWS Bedrock, LangChain). Why this is a great move 🌳 Mission & impact: Work on projects where data-driven decisions have real-world consequences. Growth: Multiple openings from mid More ❯

Foundry (full training provided). Familiarity with AI/ML Ops pipelines , real-time analytics, or edge deployments. Big Data stack knowledge (e.g., Hadoop, Spark, Kafka). GenAI/LLM experience (e.g., AWS Bedrock, LangChain). Why this is a great move 🌳 Mission & impact: Work on projects where data-driven decisions have real-world consequences. Growth: Multiple openings from mid More ❯

what is key is that you will be deeply involved with productionising and deploying. Strong Python, SQL and programming skills Hands on experience working within areas such as NLP, LLM, recommendation systems Information Retrieval stacks, GenAI (specifically RAG systems). Familiar with architecture and implementation of 1+ ML frameworks (PyTorch, scikit-learn). Experience working in the full model lifecycle More ❯

what is key is that you will be deeply involved with productionising and deploying. Strong Python, SQL and programming skills Hands on experience working within areas such as NLP, LLM, recommendation systems Information Retrieval stacks, GenAI (specifically RAG systems). Familiar with architecture and implementation of 1+ ML frameworks (PyTorch, scikit-learn). Experience working in the full model lifecycle More ❯

marketing technology, or digital enablement roles, with at least 2 years working with AI tools Hands-on experience with AI-driven platforms like Drift, Conversica, 6sense, and/or LLM/NLP enhancement tools. Crucially, the ability to understand how these tools integrate (data flow, API concepts) is essential, even if they're not building the integrations from scratch. Familiarity More ❯

years of experience in designing, developing, deploying, and monitoring machine learning and GenAI solutions 2+ years of experience in Application development, deployment, and monitoring on Kubernetes Deep knowledge of LLM architectures and attention mechanisms Education: Bachelor’s/Master’s/PHD degree in any of the following: Engineering, statistics, machine learning, computational linguistics or relevant areas Commodity Trading Expertise More ❯

years of experience in designing, developing, deploying, and monitoring machine learning and GenAI solutions 2+ years of experience in Application development, deployment, and monitoring on Kubernetes Deep knowledge of LLM architectures and attention mechanisms Education: Bachelor’s/Master’s/PHD degree in any of the following: Engineering, statistics, machine learning, computational linguistics or relevant areas Commodity Trading Expertise More ❯

/PySpark/Databricks or distributed data experience Familiarity with AWS (S3, EMR) or Hive-based environments Consulting or enterprise B2B experience Exposure to causal AI, agentic systems, or LLM applications 💼 Why Join Join a multi-award-winning AI innovator operating at global scale Work on meaningful, high-impact AI projects with blue-chip clients Hybrid setup — 3 days per More ❯

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. More ❯

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. More ❯

collaboratively with the team to ensure project deadlines are met. 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 More ❯

agentic platform. Key Responsibilities Collaborate with client teams to understand domain challenges and success metrics. Translate business requirements into scalable AI solutions using the agentic framework. Prototype and deploy LLM-based agents (prompt structures, orchestration logic, memory, retrieval pipelines). Drive the transition from proof-of-concept to production with reliability and measurable ROI. Act as a trusted technical advisor More ❯

agentic platform. Key Responsibilities Collaborate with client teams to understand domain challenges and success metrics. Translate business requirements into scalable AI solutions using the agentic framework. Prototype and deploy LLM-based agents (prompt structures, orchestration logic, memory, retrieval pipelines). Drive the transition from proof-of-concept to production with reliability and measurable ROI. Act as a trusted technical advisor More ❯