1 to 25 of 3,430 Machine Learning Jobs in the UK

predictions and solving complex problems with machine learning techniques. Always with an ethical and accurate lens. A Machine Learning & AI Engineer designing and deploying robust ML systems that bring data science to life through automation, CI/CD, and modern cloud engineering practices. Wherever you land, youll be working with some of the biggest datasets in … deploy machine learning models for fraud detection, credit risk, customer segmentation, and behavioural analytics using scalable frameworks like TensorFlow, PyTorch, and XGBoost. Engineer robust data pipelines and ML workflows using Apache Spark, Vertex AI, and CI/CD tooling to ensure seamless model delivery and monitoring. Apply advanced techniques in deep learning, natural language processing (NLP), and … it. Apply it. Keep going Your personal learning plan could include: Up to three Stanford Artificial Intelligence Professional Programmes Google Cloud certifications Coursera courses on everything from advanced ML to AI ethics and explainability. Because your career is more than your day job, youll get stuck into side-of-the-desk projects to build your network, test fresh ideas More ❯

predictions and solving complex problems with machine learning techniques. Always with an ethical and accurate lens. A Machine Learning & AI Engineer designing and deploying robust ML systems that bring data science to life through automation, CI/CD, and modern cloud engineering practices. Wherever you land, youll be working with some of the biggest datasets in … deploy machine learning models for fraud detection, credit risk, customer segmentation, and behavioural analytics using scalable frameworks like TensorFlow, PyTorch, and XGBoost. Engineer robust data pipelines and ML workflows using Apache Spark, Vertex AI, and CI/CD tooling to ensure seamless model delivery and monitoring. Apply advanced techniques in deep learning, natural language processing (NLP), and … it. Apply it. Keep going Your personal learning plan could include: Up to three Stanford Artificial Intelligence Professional Programmes Google Cloud certifications Coursera courses on everything from advanced ML to AI ethics and explainability. Because your career is more than your day job, youll get stuck into side-of-the-desk projects to build your network, test fresh ideas More ❯

predictions and solving complex problems with machine learning techniques. Always with an ethical and accurate lens. A Machine Learning & AI Engineer designing and deploying robust ML systems that bring data science to life through automation, CI/CD, and modern cloud engineering practices. Wherever you land, youll be working with some of the biggest datasets in … deploy machine learning models for fraud detection, credit risk, customer segmentation, and behavioural analytics using scalable frameworks like TensorFlow, PyTorch, and XGBoost. Engineer robust data pipelines and ML workflows using Apache Spark, Vertex AI, and CI/CD tooling to ensure seamless model delivery and monitoring. Apply advanced techniques in deep learning, natural language processing (NLP), and … it. Apply it. Keep going Your personal learning plan could include: Up to three Stanford Artificial Intelligence Professional Programmes Google Cloud certifications Coursera courses on everything from advanced ML to AI ethics and explainability. Because your career is more than your day job, youll get stuck into side-of-the-desk projects to build your network, test fresh ideas More ❯

predictions and solving complex problems with machine learning techniques. Always with an ethical and accurate lens. A Machine Learning & AI Engineer designing and deploying robust ML systems that bring data science to life through automation, CI/CD, and modern cloud engineering practices. Wherever you land, youll be working with some of the biggest datasets in … deploy machine learning models for fraud detection, credit risk, customer segmentation, and behavioural analytics using scalable frameworks like TensorFlow, PyTorch, and XGBoost. Engineer robust data pipelines and ML workflows using Apache Spark, Vertex AI, and CI/CD tooling to ensure seamless model delivery and monitoring. Apply advanced techniques in deep learning, natural language processing (NLP), and … it. Apply it. Keep going Your personal learning plan could include: Up to three Stanford Artificial Intelligence Professional Programmes Google Cloud certifications Coursera courses on everything from advanced ML to AI ethics and explainability. Because your career is more than your day job, youll get stuck into side-of-the-desk projects to build your network, test fresh ideas More ❯

tools like auto-scaling, Infrastructure as Code, and continuous delivery methodologies to optimize performance and accelerate delivery Key Responsibilities: Machine Learning Solution Development: Design, develop and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy … use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as-code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on … model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of ML projects Client & Stakeholder Collaboration: Collaborate with project managers and stakeholders across a range of sectors to gather requirements and translate business needs into technical solutions. Present findings and ML model results to non-technical audiences More ❯

tools like auto-scaling, Infrastructure as Code, and continuous delivery methodologies to optimize performance and accelerate delivery Key Responsibilities: Machine Learning Solution Development: Design, develop and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy … use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as-code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on … model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of ML projects Client & Stakeholder Collaboration: Collaborate with project managers and stakeholders across a range of sectors to gather requirements and translate business needs into technical solutions. Present findings and ML model results to non-technical audiences More ❯

tools like auto-scaling, Infrastructure as Code, and continuous delivery methodologies to optimize performance and accelerate delivery Key Responsibilities: Machine Learning Solution Development: Design, develop and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy … use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as-code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on … model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of ML projects Client & Stakeholder Collaboration: Collaborate with project managers and stakeholders across a range of sectors to gather requirements and translate business needs into technical solutions. Present findings and ML model results to non-technical audiences More ❯

tools like auto-scaling, Infrastructure as Code, and continuous delivery methodologies to optimize performance and accelerate delivery Key Responsibilities: Machine Learning Solution Development: Design, develop and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy … use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as-code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on … model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of ML projects Client & Stakeholder Collaboration: Collaborate with project managers and stakeholders across a range of sectors to gather requirements and translate business needs into technical solutions. Present findings and ML model results to non-technical audiences More ❯

tools like auto-scaling, Infrastructure as Code, and continuous delivery methodologies to optimize performance and accelerate delivery Key Responsibilities: Machine Learning Solution Development: Design, develop and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy … use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as-code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on … model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of ML projects Client & Stakeholder Collaboration: Collaborate with project managers and stakeholders across a range of sectors to gather requirements and translate business needs into technical solutions. Present findings and ML model results to non-technical audiences More ❯

tools like auto-scaling, Infrastructure as Code, and continuous delivery methodologies to optimize performance and accelerate delivery Key Responsibilities: Machine Learning Solution Development: Design, develop and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy … use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as-code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on … model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of ML projects Client & Stakeholder Collaboration: Collaborate with project managers and stakeholders across a range of sectors to gather requirements and translate business needs into technical solutions. Present findings and ML model results to non-technical audiences More ❯

tools like auto-scaling, Infrastructure as Code, and continuous delivery methodologies to optimize performance and accelerate delivery Key Responsibilities: Machine Learning Solution Development: Design, develop and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy … use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as-code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on … model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of ML projects Client & Stakeholder Collaboration: Collaborate with project managers and stakeholders across a range of sectors to gather requirements and translate business needs into technical solutions. Present findings and ML model results to non-technical audiences More ❯

tools like auto-scaling, Infrastructure as Code, and continuous delivery methodologies to optimize performance and accelerate delivery Key Responsibilities: Machine Learning Solution Development: Design, develop and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy … use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as-code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on … model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of ML projects Client & Stakeholder Collaboration: Collaborate with project managers and stakeholders across a range of sectors to gather requirements and translate business needs into technical solutions. Present findings and ML model results to non-technical audiences More ❯

tools like auto-scaling, Infrastructure as Code, and continuous delivery methodologies to optimize performance and accelerate delivery Key Responsibilities: Machine Learning Solution Development: Design, develop and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy … use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as-code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on … model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of ML projects Client & Stakeholder Collaboration: Collaborate with project managers and stakeholders across a range of sectors to gather requirements and translate business needs into technical solutions. Present findings and ML model results to non-technical audiences More ❯

tools like auto-scaling, Infrastructure as Code, and continuous delivery methodologies to optimize performance and accelerate delivery Key Responsibilities: Machine Learning Solution Development: Design, develop and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy … use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as-code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on … model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of ML projects Client & Stakeholder Collaboration: Collaborate with project managers and stakeholders across a range of sectors to gather requirements and translate business needs into technical solutions. Present findings and ML model results to non-technical audiences More ❯

tools like auto-scaling, Infrastructure as Code, and continuous delivery methodologies to optimize performance and accelerate delivery Key Responsibilities: Machine Learning Solution Development: Design, develop and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy … use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as-code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on … model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of ML projects Client & Stakeholder Collaboration: Collaborate with project managers and stakeholders across a range of sectors to gather requirements and translate business needs into technical solutions. Present findings and ML model results to non-technical audiences More ❯

tools like auto-scaling, Infrastructure as Code, and continuous delivery methodologies to optimize performance and accelerate delivery Key Responsibilities: Machine Learning Solution Development: Design, develop and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy … use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as-code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on … model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of ML projects Client & Stakeholder Collaboration: Collaborate with project managers and stakeholders across a range of sectors to gather requirements and translate business needs into technical solutions. Present findings and ML model results to non-technical audiences More ❯

tools like auto-scaling, Infrastructure as Code, and continuous delivery methodologies to optimize performance and accelerate delivery Key Responsibilities: Machine Learning Solution Development: Design, develop and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy … use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as-code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on … model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of ML projects Client & Stakeholder Collaboration: Collaborate with project managers and stakeholders across a range of sectors to gather requirements and translate business needs into technical solutions. Present findings and ML model results to non-technical audiences More ❯

tools like auto-scaling, Infrastructure as Code, and continuous delivery methodologies to optimize performance and accelerate delivery Key Responsibilities: Machine Learning Solution Development: Design, develop and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy … use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as-code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on … model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of ML projects Client & Stakeholder Collaboration: Collaborate with project managers and stakeholders across a range of sectors to gather requirements and translate business needs into technical solutions. Present findings and ML model results to non-technical audiences More ❯

tools like auto-scaling, Infrastructure as Code, and continuous delivery methodologies to optimize performance and accelerate delivery Key Responsibilities: Machine Learning Solution Development: Design, develop and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy … use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as-code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on … model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of ML projects Client & Stakeholder Collaboration: Collaborate with project managers and stakeholders across a range of sectors to gather requirements and translate business needs into technical solutions. Present findings and ML model results to non-technical audiences More ❯

tools like auto-scaling, Infrastructure as Code, and continuous delivery methodologies to optimize performance and accelerate delivery Key Responsibilities: Machine Learning Solution Development: Design, develop and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy … use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as-code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on … model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of ML projects Client & Stakeholder Collaboration: Collaborate with project managers and stakeholders across a range of sectors to gather requirements and translate business needs into technical solutions. Present findings and ML model results to non-technical audiences More ❯

tools like auto-scaling, Infrastructure as Code, and continuous delivery methodologies to optimize performance and accelerate delivery Key Responsibilities: Machine Learning Solution Development: Design, develop and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy … use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as-code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on … model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of ML projects Client & Stakeholder Collaboration: Collaborate with project managers and stakeholders across a range of sectors to gather requirements and translate business needs into technical solutions. Present findings and ML model results to non-technical audiences More ❯

tools like auto-scaling, Infrastructure as Code, and continuous delivery methodologies to optimize performance and accelerate delivery Key Responsibilities: Machine Learning Solution Development: Design, develop and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy … use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as-code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on … model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of ML projects Client & Stakeholder Collaboration: Collaborate with project managers and stakeholders across a range of sectors to gather requirements and translate business needs into technical solutions. Present findings and ML model results to non-technical audiences More ❯

tools like auto-scaling, Infrastructure as Code, and continuous delivery methodologies to optimize performance and accelerate delivery Key Responsibilities: Machine Learning Solution Development: Design, develop and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy … use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as-code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on … model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of ML projects Client & Stakeholder Collaboration: Collaborate with project managers and stakeholders across a range of sectors to gather requirements and translate business needs into technical solutions. Present findings and ML model results to non-technical audiences More ❯

tools like auto-scaling, Infrastructure as Code, and continuous delivery methodologies to optimize performance and accelerate delivery Key Responsibilities: Machine Learning Solution Development: Design, develop and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy … use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as-code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on … model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of ML projects Client & Stakeholder Collaboration: Collaborate with project managers and stakeholders across a range of sectors to gather requirements and translate business needs into technical solutions. Present findings and ML model results to non-technical audiences More ❯

tools like auto-scaling, Infrastructure as Code, and continuous delivery methodologies to optimize performance and accelerate delivery Key Responsibilities: Machine Learning Solution Development: Design, develop and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy … use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as-code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on … model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of ML projects Client & Stakeholder Collaboration: Collaborate with project managers and stakeholders across a range of sectors to gather requirements and translate business needs into technical solutions. Present findings and ML model results to non-technical audiences More ❯