1 to 25 of 973 LLM Jobs in the UK excluding London

are proficient in developing data pipelines for large, complex datasets using Python, SQL, NoSQL, and big data tools, preferably on AWS or Azure. You have hands-on experience with LLM operations and agentic AI tools such as GPT-5, LangChain, and vector databases. You understand MLOps, including deployment, monitoring, and CI/CD pipelines, and can effectively communicate complex technical More ❯

designing, developing, deploying, and monitoring machine learning and GenAI solutions Experience working with data scientists and/or ML engineers and building auto-scaling ML systems Deep knowledge of LLM architectures and attention mechanisms – experienced with MoE, MHA, SWA, Flash Attention, GPT, LLaMA, DeepSeek, and reasoning-based models Experience in creating and maintaining RAG supported systems including PDF extraction, OCR More ❯

designing, developing, deploying, and monitoring machine learning and GenAI solutions Experience working with data scientists and/or ML engineers and building auto-scaling ML systems Deep knowledge of LLM architectures and attention mechanisms – experienced with MoE, MHA, SWA, Flash Attention, GPT, LLaMA, DeepSeek, and reasoning-based models Experience in creating and maintaining RAG supported systems including PDF extraction, OCR More ❯

designing, developing, deploying, and monitoring machine learning and GenAI solutions Experience working with data scientists and/or ML engineers and building auto-scaling ML systems Deep knowledge of LLM architectures and attention mechanisms – experienced with MoE, MHA, SWA, Flash Attention, GPT, LLaMA, DeepSeek, and reasoning-based models Experience in creating and maintaining RAG supported systems including PDF extraction, OCR More ❯

AI Solutions Engineer AI Solutions, B2B, B2C, Azure, AI Foundry, Open-AI, Microsoft Copilot Studio, Machine Learning, Python, TensorFlow, PyTorch, scikit-learn, Large Language Models, LLM, Data preprocessing, REST API, Microservices architecture, MLOps, CI/CD for ML, Power-BI, Docker, Kubernetes, AI Ethics, Cloud Platforms, AWS, Google Cloud Platform, SQL, NoSQL, DevOps, Financial services, Regulatory environments Contract Type: Hybrid More ❯

include engaging in discussions with customers and understanding their generative AI needs and using platform tools and packages to finetune and iterate on modeling experiments using Large Language Models (LLM) or Retrieval Augmented Generation (RAG). Your experiments should be focused around data and metrics that drive continuous improvement. This includes designing data-driven experiments focused on optimizing model performance More ❯

analytics services such as Amazon EC2, AWS Lambda, AWS Fargate, Amazon ECS, Amazon EKS, Amazon S3, AWS Glue, Amazon RDS, Amazon DynamoDB, Amazon Aurora, Amazon SageMaker, Amazon Bedrock (including LLM hosting and management). Expertise in workflow orchestration tools such as Apache Airflow Experience implementing DataOps best practices and tooling, including DataOps.Live Advanced skills in data storage and management platforms More ❯

ready applications that drive automation, optimisation, and intelligence 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 More ❯

ready applications that drive automation, optimisation, and intelligence 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 More ❯

ready applications that drive automation, optimisation, and intelligence 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 More ❯

across data science, engineering, and business teams to shape and implement transformative AI strategies, from concept through to scalable deployment. What you’ll do: ● Lead design and architecture of LLM-powered and multi-agent AI systems ● Advise senior stakeholders and run AI strategy workshops ● Oversee end-to-end solution delivery across industries such as finance, insurance, and utilities ● Mentor teams More ❯

across data science, engineering, and business teams to shape and implement transformative AI strategies, from concept through to scalable deployment. What you’ll do: ● Lead design and architecture of LLM-powered and multi-agent AI systems ● Advise senior stakeholders and run AI strategy workshops ● Oversee end-to-end solution delivery across industries such as finance, insurance, and utilities ● Mentor teams More ❯

across data science, engineering, and business teams to shape and implement transformative AI strategies, from concept through to scalable deployment. What you’ll do: ● Lead design and architecture of LLM-powered and multi-agent AI systems ● Advise senior stakeholders and run AI strategy workshops ● Oversee end-to-end solution delivery across industries such as finance, insurance, and utilities ● Mentor teams More ❯

and NLP. Strong programming skills in Python; 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 More ❯

and NLP. Strong programming skills in Python; 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 More ❯

and NLP. Strong programming skills in Python; 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 More ❯

and data science. Experience building, deploying, and scaling AI models in Azure and/or AWS environments. Skilled in using LangChain, LangGraph, vector databases, and prompt engineering. Understanding of LLM optimisation, including context extension and RAG-based frameworks. Proven ability to collaborate cross-functionally and communicate complex AI concepts to non-technical teams. A proactive, creative mindset — comfortable operating with More ❯

and data science. Experience building, deploying, and scaling AI models in Azure and/or AWS environments. Skilled in using LangChain, LangGraph, vector databases, and prompt engineering. Understanding of LLM optimisation, including context extension and RAG-based frameworks. Proven ability to collaborate cross-functionally and communicate complex AI concepts to non-technical teams. A proactive, creative mindset — comfortable operating with More ❯

and data science. Experience building, deploying, and scaling AI models in Azure and/or AWS environments. Skilled in using LangChain, LangGraph, vector databases, and prompt engineering. Understanding of LLM optimisation, including context extension and RAG-based frameworks. Proven ability to collaborate cross-functionally and communicate complex AI concepts to non-technical teams. A proactive, creative mindset — comfortable operating with More ❯

Azure/GCP), CI/CD, Docker/Kubernetes; workflow orchestration (Airflow/Prefect); experiment tracking (MLflow/W&B). Machine Learning for time series/tabular data; LLM/agentic systems, retrieval pipelines, and evaluation/guardrails. Research driven, rigorous and curious; clear scientific writing with a publication mindset. Ability to communicate complex ideas to non-technical audiences More ❯

are looking for: Research engineering: 5+ years of industry experience in complex machine learning projects including data processing and training models in a distributed environment. LLMs: Deep understanding of LLM architectures, pre and/or post-training, evaluations and inference. Programming languages: Excellent proficiency in Python using PyTorch/TensorFlow/JAX framework. Customer facing: Experience working directly with customers More ❯

are looking for: Research engineering: 5+ years of industry experience in complex machine learning projects including data processing and training models in a distributed environment. LLMs: Deep understanding of LLM architectures, pre and/or post-training, evaluations and inference. Programming languages: Excellent proficiency in Python using PyTorch/TensorFlow/JAX framework. Customer facing: Experience working directly with customers More ❯

are looking for: Research engineering: 5+ years of industry experience in complex machine learning projects including data processing and training models in a distributed environment. LLMs: Deep understanding of LLM architectures, pre and/or post-training, evaluations and inference. Programming languages: Excellent proficiency in Python using PyTorch/TensorFlow/JAX framework. Customer facing: Experience working directly with customers More ❯

Docker, Kubernetes). This role involves building scalable AI/ML systems, deploying LLMs, and operationalizing models in production. Key Responsibilities: Design, develop, and deploy ML, Deep Learning, and LLM solutions. Implement scalable ML and data pipelines in Databricks (PySpark, Delta Lake, MLflow). Build automated MLOps pipelines with model tracking, CI/CD, and registry. Deploy and operationalize LLMs More ❯

Docker, Kubernetes). This role involves building scalable AI/ML systems, deploying LLMs, and operationalizing models in production. Key Responsibilities: Design, develop, and deploy ML, Deep Learning, and LLM solutions. Implement scalable ML and data pipelines in Databricks (PySpark, Delta Lake, MLflow). Build automated MLOps pipelines with model tracking, CI/CD, and registry. Deploy and operationalize LLMs More ❯