1 to 25 of 3,035 Permanent Machine Learning Jobs in the UK

less environmental impact. .# Job Description # Key Responsibilities May Include: Collaborate with key stakeholders to identify business challenges, translating ambiguous problems into structured analyses using statistical modelling and machine learning algorithms. Lead the selection, validation, and optimization of models to discover meaningful patterns and insights, ensuring models remain relevant, reliable, and scalable. Drive continuous integration and deployment … of data science solutions, optimizing performance through advanced machine learning techniques, code reviews, and best practices. 'Develop and deliver sophisticated visualizations, dashboards, and reports translate complex data into clear, actionable insights for business stakeholders. Present technical solutions to business stakeholders, using creative methods to explain complex concepts, increase understanding, and encourage solution adoption. Mentor and develop junior data … and experience following CRISP-DM data science lifecycle. Expertise taking projects from ideation or experimental Jupyter notebooks to full production deployment. Strong programming skills in Python, with familiarity in ML libraries/frameworks such as TensorFlow, PyTorch, and Scikit-learn. Experience with MLOps practices including model drift detection, decay, A/B testing, integration testing, differential testing, Python package building More ❯

Kafka, Airflow, and Spark to build scalable and efficient data pipelines Ability to design, build, and deploy data solutions that capture, explore, transform, and utilize data to support AI, ML, and BI Strong ability in programming languages such as Java, Python, and C/C++ Ability in data science languages/tools such as SQL, R, SAS, or Excel Proficiency More ❯

the development and expansion of our global analytics platform —supporting Front Office Trading across commodities—by building scalable, secure, and efficient data solutions. You will work alongside data scientists, ML engineers, and business stakeholders to understand requirements, design and build robust data pipelines, and deliver end-to-end analytics and ML/AI capabilities. Key Responsibilities Design, build, and maintain … Databricks on AWS. Develop and enhance the Front Office data warehouse to ensure performance, reliability, and data quality for trading analytics. Partner with data scientists and quants to prepare ML-ready datasets and support the development of production-grade ML/AI pipelines. Implement and maintain CI/CD pipelines, testing frameworks, and observability tools for data engineering workflows. Contribute … as Code (IaC) using Terraform for provisioning data infrastructure, including permissions, clusters, jobs, and lakehouse resources. Proven experience in building MLOps pipelines, tracking model lifecycle, and integrating with modern ML frameworks (e.g., scikit-learn, XGBoost, TensorFlow). Exposure to streaming data pipelines (e.g., Kafka, Structured Streaming) and real-time analytics architectures is a strong plus. Experience implementing robust DevOps practices More ❯

the development and expansion of our global analytics platform —supporting Front Office Trading across commodities—by building scalable, secure, and efficient data solutions. You will work alongside data scientists, ML engineers, and business stakeholders to understand requirements, design and build robust data pipelines, and deliver end-to-end analytics and ML/AI capabilities. Key Responsibilities Design, build, and maintain … Databricks on AWS. Develop and enhance the Front Office data warehouse to ensure performance, reliability, and data quality for trading analytics. Partner with data scientists and quants to prepare ML-ready datasets and support the development of production-grade ML/AI pipelines. Implement and maintain CI/CD pipelines, testing frameworks, and observability tools for data engineering workflows. Contribute … as Code (IaC) using Terraform for provisioning data infrastructure, including permissions, clusters, jobs, and lakehouse resources. Proven experience in building MLOps pipelines, tracking model lifecycle, and integrating with modern ML frameworks (e.g., scikit-learn, XGBoost, TensorFlow). Exposure to streaming data pipelines (e.g., Kafka, Structured Streaming) and real-time analytics architectures is a strong plus. Experience implementing robust DevOps practices More ❯

the development and expansion of our global analytics platform —supporting Front Office Trading across commodities—by building scalable, secure, and efficient data solutions. You will work alongside data scientists, ML engineers, and business stakeholders to understand requirements, design and build robust data pipelines, and deliver end-to-end analytics and ML/AI capabilities. Key Responsibilities Design, build, and maintain … Databricks on AWS. Develop and enhance the Front Office data warehouse to ensure performance, reliability, and data quality for trading analytics. Partner with data scientists and quants to prepare ML-ready datasets and support the development of production-grade ML/AI pipelines. Implement and maintain CI/CD pipelines, testing frameworks, and observability tools for data engineering workflows. Contribute … as Code (IaC) using Terraform for provisioning data infrastructure, including permissions, clusters, jobs, and lakehouse resources. Proven experience in building MLOps pipelines, tracking model lifecycle, and integrating with modern ML frameworks (e.g., scikit-learn, XGBoost, TensorFlow). Exposure to streaming data pipelines (e.g., Kafka, Structured Streaming) and real-time analytics architectures is a strong plus. Experience implementing robust DevOps practices More ❯

the development and expansion of our global analytics platform —supporting Front Office Trading across commodities—by building scalable, secure, and efficient data solutions. You will work alongside data scientists, ML engineers, and business stakeholders to understand requirements, design and build robust data pipelines, and deliver end-to-end analytics and ML/AI capabilities. Key Responsibilities Design, build, and maintain … Databricks on AWS. Develop and enhance the Front Office data warehouse to ensure performance, reliability, and data quality for trading analytics. Partner with data scientists and quants to prepare ML-ready datasets and support the development of production-grade ML/AI pipelines. Implement and maintain CI/CD pipelines, testing frameworks, and observability tools for data engineering workflows. Contribute … as Code (IaC) using Terraform for provisioning data infrastructure, including permissions, clusters, jobs, and lakehouse resources. Proven experience in building MLOps pipelines, tracking model lifecycle, and integrating with modern ML frameworks (e.g., scikit-learn, XGBoost, TensorFlow). Exposure to streaming data pipelines (e.g., Kafka, Structured Streaming) and real-time analytics architectures is a strong plus. Experience implementing robust DevOps practices More ❯

the development and expansion of our global analytics platform —supporting Front Office Trading across commodities—by building scalable, secure, and efficient data solutions. You will work alongside data scientists, ML engineers, and business stakeholders to understand requirements, design and build robust data pipelines, and deliver end-to-end analytics and ML/AI capabilities. Key Responsibilities Design, build, and maintain … Databricks on AWS. Develop and enhance the Front Office data warehouse to ensure performance, reliability, and data quality for trading analytics. Partner with data scientists and quants to prepare ML-ready datasets and support the development of production-grade ML/AI pipelines. Implement and maintain CI/CD pipelines, testing frameworks, and observability tools for data engineering workflows. Contribute … as Code (IaC) using Terraform for provisioning data infrastructure, including permissions, clusters, jobs, and lakehouse resources. Proven experience in building MLOps pipelines, tracking model lifecycle, and integrating with modern ML frameworks (e.g., scikit-learn, XGBoost, TensorFlow). Exposure to streaming data pipelines (e.g., Kafka, Structured Streaming) and real-time analytics architectures is a strong plus. Experience implementing robust DevOps practices More ❯

More Inclusive At BJAK, we're using AI to make insurance and financial services easier to access, understand, and afford for millions across Southeast Asia. As a AI/ML Software Engineer, you'll help build the intelligent systems that power this mission - from personalized recommendations and fraud detection to automation and search. This is a remote role based in … work will directly impact user experience, efficiency, and platform intelligence You'll contribute to production-grade AI systems that serve millions You'll collaborate across functions and build scalable ML tools from the ground up You'll grow quickly in a lean, high-impact engineering environment What You'll Do Work with product, data, and engineering teams to define ML … goals and technical strategies Design, build, and deploy machine learning models that power personalization, automation, and insight generation Manage the ML lifecycle: data preprocessing, feature engineering, model training, evaluation, and deployment Build scalable ML infrastructure and deployment pipelines Integrate ML outputs into user-facing products and backend systems Stay current with AI/ML trends and apply relevant More ❯

Bloomsbury Square, London, United Kingdom Software Engineer - AI/ML/Python (Lead Level) at N Consulting Ltd Job Title: Software Engineer - AI/ML/Python (Lead Level) Location: London, United Kingdom (Hybrid - 2-3 days onsite per week) Contract Type: Contract (6 months, with extension likely) Start Date: Immediate/Within 2-4 weeks Job Description: We are … seeking an experienced and highly motivated Lead Software Engineer with deep expertise in Artificial Intelligence (AI) , Machine Learning (ML) , and Python development to lead the design, development, and deployment of intelligent systems and data-driven applications for a leading client in London. The ideal candidate will have a strong background in AI/ML frameworks, scalable system design … and Python-based development, along with leadership experience in agile teams. Key Responsibilities: Lead the architecture and development of AI/ML solutions, ensuring scalable and efficient design. Design and implement ML models and algorithms (classification, regression, NLP, etc.) using modern frameworks. Collaborate with data scientists, engineers, and product teams to transform prototypes into production-grade applications. Optimize model performance More ❯

world. Data Analytics & AI - Data Science & AI - Senior Consultant EY exists to build a better working world. We empower our people by offering the culture, tech, teams, scale, challenges, learning, and the relationships for you to personalise and build your career, helping to create long-term value for clients, people and society and build trust in the capital markets. … interpret data, and support data driven decision making. Machine Learning and AI Model End to End Management : The ability to build and manage the full lifecycle of ML models including development, validation, deployment, and monitoring in production environments. Collaboration : The ability to work closely with cross-functional teams to develop, test, and deploy advanced machine learning … Proficiency in Python, SQL, and deep learning frameworks such as TensorFlow and PyTorch. Programming: Solid experience in Python and SQL. Experience with R is a nice-to-have. ML and AI: Practical experience using ML modeling libraries like Scikit-Learn, Keras, Tensorflow, PyTorch and similar Generative AI: Some hands-on experience with LLMs for prompt engineering or agents is More ❯

Learning, or a related field. 6-8 years of professional, hands-on experience in software engineering, with a primary focus on building and deploying complex AI/ML systems into production. A proven track record of delivering scalable, reliable, and high-performance software solutions from concept to deployment. For more information on how we process your personal data More ❯

Learning, or a related field. 6-8 years of professional, hands-on experience in software engineering, with a primary focus on building and deploying complex AI/ML systems into production. A proven track record of delivering scalable, reliable, and high-performance software solutions from concept to deployment. For more information on how we process your personal data More ❯

learning models and AI-driven solutions to address business challenges. Collaborate with data scientists to transition prototypes into production-ready systems . Develop and maintain end-to-end ML pipelines for data ingestion, training, testing, and deployment. Optimise model performance, scalability, and reliability using MLOps best practices. Work with large-scale structured and unstructured datasets for model training and … validation. Implement model monitoring, versioning, and retraining processes to ensure continuous improvement. Collaborate cross-functionally with engineering, data, and product teams to integrate ML solutions into production environments. Stay current with emerging trends in AI/ML technologies and contribute to innovation within the organisation. Required Skills & Experience Proven experience (3–5+ years) as a Machine Learning Engineer … lakes, and cloud data platforms. Proficiency with cloud services (AWS, Azure, or GCP) for model deployment and orchestration. Knowledge of containerization and orchestration tools (Docker, Kubernetes). Experience integrating ML models into production environments via APIs or microservices. Excellent problem-solving, analytical, and communication skills. Preferred Qualifications Bachelor’s or Master’s degree in Computer Science , Data Science , Mathematics , or More ❯

learning models and AI-driven solutions to address business challenges. Collaborate with data scientists to transition prototypes into production-ready systems . Develop and maintain end-to-end ML pipelines for data ingestion, training, testing, and deployment. Optimise model performance, scalability, and reliability using MLOps best practices. Work with large-scale structured and unstructured datasets for model training and … validation. Implement model monitoring, versioning, and retraining processes to ensure continuous improvement. Collaborate cross-functionally with engineering, data, and product teams to integrate ML solutions into production environments. Stay current with emerging trends in AI/ML technologies and contribute to innovation within the organisation. Required Skills & Experience Proven experience (3–5+ years) as a Machine Learning Engineer … lakes, and cloud data platforms. Proficiency with cloud services (AWS, Azure, or GCP) for model deployment and orchestration. Knowledge of containerization and orchestration tools (Docker, Kubernetes). Experience integrating ML models into production environments via APIs or microservices. Excellent problem-solving, analytical, and communication skills. Preferred Qualifications Bachelor’s or Master’s degree in Computer Science , Data Science , Mathematics , or More ❯

learning models and AI-driven solutions to address business challenges. Collaborate with data scientists to transition prototypes into production-ready systems . Develop and maintain end-to-end ML pipelines for data ingestion, training, testing, and deployment. Optimise model performance, scalability, and reliability using MLOps best practices. Work with large-scale structured and unstructured datasets for model training and … validation. Implement model monitoring, versioning, and retraining processes to ensure continuous improvement. Collaborate cross-functionally with engineering, data, and product teams to integrate ML solutions into production environments. Stay current with emerging trends in AI/ML technologies and contribute to innovation within the organisation. Required Skills & Experience Proven experience (3–5+ years) as a Machine Learning Engineer … lakes, and cloud data platforms. Proficiency with cloud services (AWS, Azure, or GCP) for model deployment and orchestration. Knowledge of containerization and orchestration tools (Docker, Kubernetes). Experience integrating ML models into production environments via APIs or microservices. Excellent problem-solving, analytical, and communication skills. Preferred Qualifications Bachelor’s or Master’s degree in Computer Science , Data Science , Mathematics , or More ❯

learning models and AI-driven solutions to address business challenges. Collaborate with data scientists to transition prototypes into production-ready systems . Develop and maintain end-to-end ML pipelines for data ingestion, training, testing, and deployment. Optimise model performance, scalability, and reliability using MLOps best practices. Work with large-scale structured and unstructured datasets for model training and … validation. Implement model monitoring, versioning, and retraining processes to ensure continuous improvement. Collaborate cross-functionally with engineering, data, and product teams to integrate ML solutions into production environments. Stay current with emerging trends in AI/ML technologies and contribute to innovation within the organisation. Required Skills & Experience Proven experience (3–5+ years) as a Machine Learning Engineer … lakes, and cloud data platforms. Proficiency with cloud services (AWS, Azure, or GCP) for model deployment and orchestration. Knowledge of containerization and orchestration tools (Docker, Kubernetes). Experience integrating ML models into production environments via APIs or microservices. Excellent problem-solving, analytical, and communication skills. Preferred Qualifications Bachelor’s or Master’s degree in Computer Science , Data Science , Mathematics , or More ❯

learning models and AI-driven solutions to address business challenges. Collaborate with data scientists to transition prototypes into production-ready systems . Develop and maintain end-to-end ML pipelines for data ingestion, training, testing, and deployment. Optimise model performance, scalability, and reliability using MLOps best practices. Work with large-scale structured and unstructured datasets for model training and … validation. Implement model monitoring, versioning, and retraining processes to ensure continuous improvement. Collaborate cross-functionally with engineering, data, and product teams to integrate ML solutions into production environments. Stay current with emerging trends in AI/ML technologies and contribute to innovation within the organisation. Required Skills & Experience Proven experience (3–5+ years) as a Machine Learning Engineer … lakes, and cloud data platforms. Proficiency with cloud services (AWS, Azure, or GCP) for model deployment and orchestration. Knowledge of containerization and orchestration tools (Docker, Kubernetes). Experience integrating ML models into production environments via APIs or microservices. Excellent problem-solving, analytical, and communication skills. Preferred Qualifications Bachelor’s or Master’s degree in Computer Science , Data Science , Mathematics , or More ❯

heart of trust! From reviews to user behaviours to internal systems, at Trustpilot we truly have big data. And, in our Applied AI team, we are leveraging AI and ML to transform data into value, drive product innovation, improve user experience, enrich Trustpilot's data ecosystem and enhance business growth. We are seeking a Data Science Manager to join our … developing a team of Data Scientists specializing in Applied AI, across junior, mid-level, and senior roles. The ideal candidate will also possess hands on experience throughout the entire ML modeling lifecycle, from initial concept to production deployment. You will collaborate with cross functional teams in product management, UX, data analytics, and engineering across various product contexts. This role involves … managing the delivery and maintenance of highly impactful, innovative AI/ML solutions at scale. To effectively fulfil this role, you will have an extensive technical background and several years of experience as a Data Scientist/Applied AI Scientist before transitioning into the leadership track. You are a lifelong learner - across technology, leadership, and ways of working - and you More ❯

requisition id: R Role Overview As an AI Science Manager in IQVIA's Applied Data Science Center (ADSC), you will lead the development and delivery of advanced AI/ML solutions for life sciences and healthcare clients. You will combine deep scientific expertise, technical leadership, and strategy consulting skills to drive impactful projects, manage client relationships, and lead multidisciplinary teams. … pivotal in shaping IQVIA's technical leadership and credibility in AI-driven healthcare transformation. Key Responsibilities Scientific & Technical Leadership Lead the design, development, and deployment of scalable AI/ML solutions, including Generative AI and Agentic AI frameworks. Fine-tune and distil large language models (LLMs) for diverse use cases; transform raw data into trainable datasets for model development. Apply … advanced techniques such as Supervised Fine-Tuning (SFT), Proximal Policy Optimisation (PPO), and reward modelling. Stay abreast of the latest advancements in AI/ML and proactively introduce emerging techniques into client solutions. Provide technical leadership and mentorship to team members, fostering a collaborative and innovative environment. Infrastructure & Solution Architecture Collaborate with Solution Architects to design and map infrastructure solutions More ❯

gaps, and implement solutions that drive efficiency and innovation. Lead migration and upgrade projects for legacy systems, ensuring minimal disruption and maximizing system adoption across teams. Leverage AI/ML and analytics capabilities to enhance reporting, forecasting, and decision-making within these platforms. Lead end-to-end digital transformation initiatives, modernizing legacy systems and migrating critical business applications to cloud … in at least two areas: Salesforce Sales Cloud, Cloud Networking, Security, AI, Storage, Private/Hybrid/Public Cloud, Data Protection, Disaster Recovery. Industry Trends: Experience with AI/ML-driven CRM solutions and cloud-native architectures More ❯

gaps, and implement solutions that drive efficiency and innovation. Lead migration and upgrade projects for legacy systems, ensuring minimal disruption and maximizing system adoption across teams. Leverage AI/ML and analytics capabilities to enhance reporting, forecasting, and decision-making within these platforms. Lead end-to-end digital transformation initiatives, modernizing legacy systems and migrating critical business applications to cloud … in at least two areas: Salesforce Sales Cloud, Cloud Networking, Security, AI, Storage, Private/Hybrid/Public Cloud, Data Protection, Disaster Recovery. Industry Trends: Experience with AI/ML-driven CRM solutions and cloud-native architectures More ❯

gaps, and implement solutions that drive efficiency and innovation. Lead migration and upgrade projects for legacy systems, ensuring minimal disruption and maximizing system adoption across teams. Leverage AI/ML and analytics capabilities to enhance reporting, forecasting, and decision-making within these platforms. Lead end-to-end digital transformation initiatives, modernizing legacy systems and migrating critical business applications to cloud … in at least two areas: Salesforce Sales Cloud, Cloud Networking, Security, AI, Storage, Private/Hybrid/Public Cloud, Data Protection, Disaster Recovery. Industry Trends: Experience with AI/ML-driven CRM solutions and cloud-native architectures More ❯

Manage multiple projects from inception to deployment within cloud-based environments. Maintain high standards in code review, documentation, and delivery in a DevOps context. Apply a deep understanding of ML techniques, from supervised/unsupervised learning to generative AI and large language models. What We’re Looking For Essential Skills and Experience: Proven ability to solve complex, real-world … Experience managing multiple end-to-end data science projects across varied data types. Familiarity with DevOps practices and tools like Git. Cloud experience (e.g. Azure, AWS) and working with ML platforms and services. Strong communication skills, capable of explaining complex topics to non-technical stakeholders. Ability to align data science efforts with broader business objectives. Desirable Skills: Experience using R … and NLP or deep learning techniques (e.g. TF-IDF, word embeddings, CNNs, RNNs). Familiarity with Generative AI and prompt engineering. Experience with Azure Databricks, MLflow, Azure ML services, Docker, Kubernetes. Exposure to Agile development environments and software engineering best practices. Experience working in large or complex organisations or regulated industries. Strong working knowledge of Excel, SQL, Power BI More ❯

Manage multiple projects from inception to deployment within cloud-based environments. Maintain high standards in code review, documentation, and delivery in a DevOps context. Apply a deep understanding of ML techniques, from supervised/unsupervised learning to generative AI and large language models. What We’re Looking For Essential Skills and Experience: Proven ability to solve complex, real-world … Experience managing multiple end-to-end data science projects across varied data types. Familiarity with DevOps practices and tools like Git. Cloud experience (e.g. Azure, AWS) and working with ML platforms and services. Strong communication skills, capable of explaining complex topics to non-technical stakeholders. Ability to align data science efforts with broader business objectives. Desirable Skills: Experience using R … and NLP or deep learning techniques (e.g. TF-IDF, word embeddings, CNNs, RNNs). Familiarity with Generative AI and prompt engineering. Experience with Azure Databricks, MLflow, Azure ML services, Docker, Kubernetes. Exposure to Agile development environments and software engineering best practices. Experience working in large or complex organisations or regulated industries. Strong working knowledge of Excel, SQL, Power BI More ❯

Manage multiple projects from inception to deployment within cloud-based environments. Maintain high standards in code review, documentation, and delivery in a DevOps context. Apply a deep understanding of ML techniques, from supervised/unsupervised learning to generative AI and large language models. What We’re Looking For Essential Skills and Experience: Proven ability to solve complex, real-world … Experience managing multiple end-to-end data science projects across varied data types. Familiarity with DevOps practices and tools like Git. Cloud experience (e.g. Azure, AWS) and working with ML platforms and services. Strong communication skills, capable of explaining complex topics to non-technical stakeholders. Ability to align data science efforts with broader business objectives. Desirable Skills: Experience using R … and NLP or deep learning techniques (e.g. TF-IDF, word embeddings, CNNs, RNNs). Familiarity with Generative AI and prompt engineering. Experience with Azure Databricks, MLflow, Azure ML services, Docker, Kubernetes. Exposure to Agile development environments and software engineering best practices. Experience working in large or complex organisations or regulated industries. Strong working knowledge of Excel, SQL, Power BI More ❯