351 to 375 of 1,501 Machine Learning Jobs in the UK excluding London

driven features, while contributing to the technical growth of the team. Responsibilities Responsibilities include: Bridge data science and software engineering to productionize AI/ML solutions. Collaborate with data scientists, software engineers, and product teams to deliver robust, scalable AI features for Workday Products. Integrate AI models into production systems … languages such as Python, Java, or C++, writing clean, efficient, and maintainable code for production-grade AI solutions. Hands-on experience deploying AI/ML models to production environments, with a good understanding of MLOps practices including CI/CD pipelines, model versioning, and monitoring. Experience with cloud platforms ...

deployment pipelines, model serving, containerisation, orchestration, and environment managementSets the standard for reliability, observability, and operational excellence across the team's production AI/ML servicesBuilds the tooling and paved paths that let AI engineers ship agentic AI and ML products safely and quicklyImplements platform-level security, secrets management … containerisation, and cloud-native deployment patternsStrong CI/CD experience and a track record of building deployment and release automationExperience serving, scaling, and monitoring ML models or data-intensive services in production (MLOps)Practical experience with observability tooling (metrics, logging, tracing) and production incident responseExperience operating ML/LLM workloads ...

languages such as Python, Java, or C++, writing clean, efficient, and maintainable code for production grade AI solutions.Hands-on experience deploying AI/ML models to production environments, with a good understanding of MLOps practices including CI/CD pipelines, model versioning, and monitoring.Experience with cloud platforms (AWS, Azure … cloud-native AI tools such as SageMaker, Vertex AI, or Azure ML Studio.Working knowledge of containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes) to build and manage scalable AI solutions.Skills in data engineering, including building data pipelines, data wrangling, and integrating AI solutions with enterprise data systems.Exposure to agile ...

languages such as Python, Java, or C++, writing clean, efficient, and maintainable code for production grade AI solutions. Hands-on experience deploying AI/ML models to production environments, with a good understanding of MLOps practices including CI/CD pipelines, model versioning, and monitoring. Experience with cloud platforms … Azure, or GCP) and cloud-native AI tools such as SageMaker, Vertex AI, or Azure ML Studio. Working knowledge of containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes) to build and manage scalable AI solutions. Skills in data engineering, including building data pipelines, data wrangling, and integrating AI solutions ...

pipelines, model serving, containerisation, orchestration, and environment management Sets the standard for reliability, observability, and operational excellence across the team's production AI/ML services Builds the tooling and paved paths that let AI engineers ship agentic AI and ML products safely and quickly Implements platform-level security, secrets … cloud-native deployment patterns Strong CI/CD experience and a track record of building deployment and release automation Experience serving, scaling, and monitoring ML models or data-intensive services in production (MLOps) Practical experience with observability tooling (metrics, logging, tracing) and production incident response Experience operating ML/ ...

language such as Java or C++), and a willingness to develop clean, maintainable code under guidance. Some exposure to deploying or supporting AI/ML models - through academic projects, internships, or early career experience - with an interest in developing MLOps skills (CI/CD, model versioning, monitoring). Awareness … risks to senior colleagues. Basic understanding of cloud platforms (AWS, Azure, or GCP); exposure to cloud-native AI tools (e.g., SageMaker, Vertex AI, Azure ML Studio) is a plus. Awareness of containerisation technologies (e.g., Docker); exposure to orchestration tools (e.g., Kubernetes) is beneficial but not essential. Emerging data engineering skills ...

language such as Java or C++), and a willingness to develop clean, maintainable code under guidance. Some exposure to deploying or supporting AI/ML models — through academic projects, internships, or early career experience — with an interest in developing MLOps skills (CI/CD, model versioning, monitoring). Awareness … risks to senior colleagues. Basic understanding of cloud platforms (AWS, Azure, or GCP); exposure to cloud-native AI tools (e.g., SageMaker, Vertex AI, Azure ML Studio) is a plus. Awareness of containerisation technologies (e.g., Docker); exposure to orchestration tools (e.g., Kubernetes) is beneficial but not essential. Emerging data engineering skills ...

such as Python, Java, or C++. Emphasis on writing clean, efficient, and maintainable code for production-grade AI solutions.Proven experience in deploying AI/ML models to production environments, ensuring scalability, reliability, and performance. Familiarity with MLOps practices, including CI/CD pipelines, model versioning, and monitoring.Comprehensive experience with cloud … platforms (AWS, Azure, or GCP) and cloud native AI tools, such as SageMaker, Vertex AI, or Azure ML Studio. Ability to design and deploy cloud-based AI solutions.Proficiency in using containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes) to build and manage scalable AI solutions.Strong skills in data engineering ...

thoughtful leader. We’re now helping to build the team around them.ML Ops/Model Deployment (CI/CD, Scaling, Robustness)As the ML Ops Engineer, you’ll focus on operationalising research models—building the systems, pipelines and tooling that take experimental work and make it stable, scalable and production … prototypes.Managing model registries, monitoring, versioning and observability.Supporting integration of research models into wider engineering systems.Ensuring reproducibility and robustness across the model lifecycle.Ideal ProfileExperience in ML Ops or production ML engineering.Strong CI/CD tooling (GitHub Actions, GitLab CI, Azure DevOps).Docker/Kubernetes and modern deployment frameworks (MLflow, Seldon, BentoML ...

Python, Java, or C++. Emphasis on writing clean, efficient, and maintainable code for production-grade AI solutions. Proven experience in deploying AI/ML models to production environments, ensuring scalability, reliability, and performance. Familiarity with MLOps practices, including CI/CD pipelines, model versioning, and monitoring. Comprehensive experience with cloud … platforms (AWS, Azure, or GCP) and cloud native AI tools, such as SageMaker, Vertex AI, or Azure ML Studio. Ability to design and deploy cloud-based AI solutions. Proficiency in using containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes) to build and manage scalable AI solutions. Strong skills ...

Python, Java, or C++. Emphasis on writing clean, efficient, and maintainable code for production-grade AI solutions. Proven experience in deploying AI/ML models to production environments, ensuring scalability, reliability, and performance. Familiarity with MLOps practices, including CI/CD pipelines, model versioning, and monitoring. Comprehensive experience with cloud … platforms (AWS, Azure, or GCP) and cloud native AI tools, such as SageMaker, Vertex AI, or Azure ML Studio. Ability to design and deploy cloud-based AI solutions. Proficiency in using containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes) to build and manage scalable AI solutions. Strong skills ...

pipelines, model serving, containerisation, orchestration, and environment management Sets the standard for reliability, observability, and operational excellence across the team\'s production AI/ML services Builds the tooling and paved paths that let AI engineers ship agentic AI and ML products safely and quickly Implements platform-level security, secrets … cloud-native deployment patterns Strong CI/CD experience and a track record of building deployment and release automation Experience serving, scaling, and monitoring ML models or data-intensive services in production (MLOps) Practical experience with observability tooling (metrics, logging, tracing) and production incident response Experience operating ML/ ...

delivery — this is your chance to shape the intelligence behind the future of work. Join RoleMapper and lead the evolution of our AI and ML capabilities that make workforce decisions fairer, smarter, and more transparent. About RoleMapper In a world where organisations are facing significant skills challenges, increasing compliance requirements … phase of growth and position RoleMapper at the forefront of AI in work design. Responsibilities Translating product vision into a feasible, scalable AI and ML roadmap that delivers measurable business value and aligns with commercial goals. Designing and scaling RoleMapper’s AI and Machine Learning models, combining leadership ...

practices (SLOs, error budgets, runbooks) and leads production incident response and post-incident review Builds and operates observability across the team's AI/ML services (metrics, logging, tracing, alerting) Automates infrastructure provisioning and configuration through infrastructure-as-code Implements operational security, secrets management, and access controls to firm-wide … environment Master's degree in Computer Science, Engineering, or a related technical field (or equivalent applied experience) Preferred qualifications, capabilities, and skills Experience operating ML/LLM workloads in production (MLOps, inference reliability, cost/performance management) Experience within financial services technology Familiarity with JPM-internal platform, cloud, and observability ...

machine learning solutions across an evolving enterprise data environment. This is a hands-on role working across the full ML lifecycle, from prototype through to production, focused on delivering scalable, value-driven outcomes. Experience requirements & Responsibilities : Develop and deploy machine learning models to solve business problems. … Translate prototypes into production-ready solutions. Build and maintain data pipelines and ML infrastructure. Prepare and analyse large datasets, extracting actionable insights. Evaluate, optimise and monitor model performance. Support the design and delivery of AI initiatives. Work within Agile teams and engage with stakeholders across the business. Essential : Strong Python ...

reliability practices (SLOs, error budgets, runbooks) and leads production incident response and post-incident reviewBuilds and operates observability across the team's AI/ML services (metrics, logging, tracing, alerting)Automates infrastructure provisioning and configuration through infrastructure-as-codeImplements operational security, secrets management, and access controls to firm-wide standardsPartners … high-availability environmentMaster's degree in Computer Science, Engineering, or a related technical field (or equivalent applied experience)Preferred qualifications, capabilities, and skillsExperience operating ML/LLM workloads in production (MLOps, inference reliability, cost/performance management)Experience within financial services technologyFamiliarity with JPM-internal platform, cloud, and observability tooling ...

Python, Java, R, and C++, applying strong software engineering principles, clean coding practices, API design, distributed computing, UNIX tooling, and scalable system design. Machine Learning and AI Fundamentals - Solid knowledge of supervised and unsupervised learning, neural networks, model evaluation, prompt engineering, code transformation and refactoring, data transformation … work with large and complex datasets, including data preprocessing, feature engineering, data quality assessment, vectorisation, embeddings, and the effective use of data science and machine learning libraries. AI Engineering and Product Delivery - Experience building AI-powered capabilities into production applications, balancing experimentation with robust engineering, security, observability, maintainability ...

Python, Java, R, and C++, applying strong software engineering principles, clean coding practices, API design, distributed computing, UNIX tooling, and scalable system design. Machine Learning and AI Fundamentals – Solid knowledge of supervised and unsupervised learning, neural networks, model evaluation, prompt engineering, code transformation and refactoring, data transformation … work with large and complex datasets, including data preprocessing, feature engineering, data quality assessment, vectorisation, embeddings, and the effective use of data science and machine learning libraries. AI Engineering and Product Delivery – Experience building AI‐powered capabilities into production applications, balancing experimentation with robust engineering, security, observability, maintainability ...

machine learning solutions across an evolving enterprise data environment. This is a hands‐on role working across the full ML lifecycle, from prototype through to production, focused on delivering scalable, value‐driven outcomes. Experience requirements & Responsibilities Develop and deploy machine learning models to solve business problems. … Translate prototypes into production‐ready solutions. Build and maintain data pipelines and ML infrastructure. Prepare and analyse large datasets, extracting actionable insights. Evaluate, optimise and monitor model performance. Support the design and delivery of AI initiatives. Work within Agile teams and engage with stakeholders across the business. Essential Strong Python ...

Embedded Machine Learning and Real-Time Sensor Classification Introduced by: KO2 Embedded Recruitment Solutions Client Location: Edinburgh Salary: £60,000 to £70,000 per annum The Opportunity Our client is building the next generation of real-time detection systems that operate at the edge of the network, where ...

analysis into clear recommendations and actionable outcomes.Evaluate emerging AI capabilities and identify where they can create value.Essential Skills/ExperienceData AnalysisProgrammingScriptingArtificial Intelligence/Machine LearningStatistical AnalysisDesirable Skills/ExperienceEssentialExperience applying data science techniques to solve complex real-world problems.Strong Python and Linux skills and experience with common data science … machine learning libraries.Experience developing, testing and deploying machine learning models.Knowledge of AI technologies, including generative AI and large language models.DesirableExperience working with large-scale datasets and distributed data processing platforms such as Hadoop or Spark.Knowledge of MLOps, CI/CD pipelines and container technologies.Experience working with ...

augmented generation (RAG), named entity recognition (NER), prompt engineering, and personalization for content extraction, search, question answering, reasoning, and recommendation. Develop LLM, NLP, and ML solutions that address client requirements and drive business transformation. Work closely with partner teams—including Business, Technology, Product Management, Strategy, and Business Management—to deploy … reliability. Communicate results effectively to business stakeholders through written reports, visualizations, and presentations. Stay current with the latest research in LLM, agentic AI, ML, and data science, and proactively identify and leverage emerging techniques to drive ongoing enhancement. Qualifications, Capabilities, and Skills Proven experience applying NLP, LLM, and ML techniques ...

have a startup-like culture nurtured by sports and social clubs, being a safe place to fail, meaning and self-fulfilment over business, and learning from our partners, suppliers, and peers. We fully embrace DevOps ways of working in our teams, and build a very broad range of capabilities … challenges! It would be great if you also had experience in some of these, but if not we'll help you with them Machine Learning software e.g. Amazon and Azure machine learning, or Google's TensorFlow Deploying and managing in Cloud Computing such ...

Data Science, based in any of our Brussels (Belgium), Slough (UK) or Raleigh (US).About the roleYou will help shape how advanced AI, machine learning, and computational methods support clinical development. You will design, validate, and deploy models that help teams make evidence-based decisions in areas such … practical coding guidance.You understand supervised, unsupervised, Bayesian, and deep learning methods, and can apply them to real-world scientific questions.You have experience with ML infrastructure, reproducible research, Linux or HPC environments, and software or platform development.You communicate clearly with scientific, technical, medical, and business partners, and enjoy working ...

forefront of innovation by joining a leading AI research firm, building ML models that power products and drive decision-making at scale. Build, train, and deploy machine learning models using Python, TensorFlow, PyTorch Work on supervised, unsupervised, and reinforcement learning problems Clean and process large datasets … extract insights and train algorithms Perform feature engineering to improve model accuracy Design ML pipelines and automate model deployment Collaborate with software engineers to integrate models into applications Use MLOps practices for monitoring, retraining, and lifecycle management Conduct A/B tests and measure real-world model performance Stay ...