1 to 25 of 74 Machine Learning Jobs in Kent

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Lead the design and analysis of A/B tests and offline … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Advanced proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving, Triton ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Lead the design and analysis of A/B tests and offline … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Advanced proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving, Triton ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Lead the design and analysis of A/B tests and offline … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Advanced proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving, Triton ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Lead the design and analysis of A/B tests and offline … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Advanced proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving, Triton ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Lead the design and analysis of A/B tests and offline … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Advanced proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving, Triton ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Lead the design and analysis of A/B tests and offline … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Advanced proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving, Triton ...

machine learning platforms in production Strong background translating data science work into reliable, real-world systems Deep hands-on experience with modern ML frameworks and tooling Expert-level Python engineering and strong software design skills Proven experience building cloud-based platforms e.g. Azure Strong background in containerised environments … Experience leading technical projects and mentoring engineers Comfortable working in fast-moving, client-facing environments If you are interested in this or other Senior Ml Engineering positions, please contact Gilad Sabari @ gilad.sabari@xcede.com ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Lead the design and analysis of A/B tests and offline … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Advanced proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving, Triton ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Lead the design and analysis of A/B tests and offline … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Advanced proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving, Triton ...

infrastructure challenges. Role Overview We are looking for a Robotics Machine Learning Engineer to join our software team and help deliver ambitious ML solutions that power real-world robotic inspection and automation. This is a hands-on, applied role where you'll build multi-modal machine learning models that operate in challenging outdoor environments, helping take ideas from prototype to field-tested deployment. You'll be involved in the full ML lifecycle: building datasets, training and evaluating modern vision models, running experiments, and supporting deployment to edge devices. Demonstrable experience is essential - this could be through ...

others. We have been recognized with: 17x Google Cloud Partner of the Year awards in the last 8 years 3x AWS AI/ML award wins 3x NVIDIA Partner of the Year titles 2x Snowflake Partner of the Year awards Recognized Leaders by Gartner, Forrester, IDC, ISG, Everest Group … Work for the third year in a row- 2021, 2022, 2023 Be part of a trailblazing team that's shaping the future of AI, ML, and cloud innovation. Your next big opportunity starts here! For more details, visit: Website or LinkedIn Page. Role: Sr Machine Learning Engineer Experience ...

seeking an experienced Machine Learning Engineer to support a Financial Services organisation on an initial 6-month contract, working on production-grade ML systems that operate in regulated, high-volume environments. This role is ideal for someone comfortable taking models from research through to deployment, with a strong … into production within a Financial Services environment Collaborate closely with Data Scientists, Software Engineers, Risk, and Product teams Build and maintain end-to-end ML pipelines (training, validation, inference, monitoring) Ensure models meet requirements around performance, resilience, and explainability Contribute to MLOps best practices, model governance, and technical standards Support ...

iteration Work closely with product, engineering, and non-technical stakeholders to embed AI into real workflows Improve the scalability, reliability, and performance of existing ML systems Review code and architecture, setting a high engineering standard Analyse data and run ad-hoc studies to generate actionable insights into marketing effectiveness What … Learning Engineer/AI Engineer in a product-led environment Advanced Python skills and hands-on experience with PyTorch, TensorFlow, or similar ML libraries Experience deploying and maintaining ML models in production Solid software engineering fundamentals (algorithms, data structures, clean code) Comfortable working with Docker and containerised systems ...

Machine Learning Engineer | Early Stage Deep Tech Start Up | London I am partnered with an early stage, London based start up that is building an AI native intelligence layer for private market investing. The platform transforms fragmented documents, deal histories, and proprietary investment data into structured, searchable intelligence … private equity, venture capital, and private credit teams. They are hiring a Founding Machine Learning Engineer to join at early stage, with real ownership over both the core technology and the product direction. This role suits someone with a strong academic background from a leading university, alongside clear ...

Title: AI/ML Research Engineer (London, UK) Location: Must be based London, UK Employment Type: Permanent Work Model: Hybrid (London) Introduction: Our client (international) is a collection of engineers and designers who want the world to connect beautifully. Their team first came together to build one of the world … every day and they're putting this knowledge to work, engineering and designing better ways to connect. They are establishing a new AI/ML engineering team in London to extend these capabilities. This is a foundational role, you will be among the first hires, helping to shape the technical ...

Permanent Remuneration (UK – Remote): £81–108k Base Salary + Discretionary Bonus + Equity We are a stealth-mode AI laboratory researching and developing Machine Learning models. The founding team consists of Cambridge graduates and former engineers at Google, Meta, Bloomberg, Microsoft and Amazon. We are backed by prominent … institutional VC funds and C-level executives of global technology companies. The role involves the design and development of backend services and infrastructure for Machine Learning workloads. You will be working closely with the Machine Learning team; however, you are not required or expected to have ...

seeking a hands-on Machine Learning Senior Staff Engineer to lead cross-functional teams building and deploying cutting-edge LLM and ML systems. In this role, you'll drive the full lifecycle of AI development — from research and large-scale model training to production deployment — while mentoring … that break the models and navigating the intersection of research, engineering, and product delivery. Roles & Responsibilities: Lead and mentor a cross-functional team of ML engineers, data scientists, and MLOps professionals. Oversee the full lifecycle of LLM and ML projects — from data collection to training, evaluation, and deployment. Collaborate with ...

predictive insights during the action. As a Lead Machine Learning Engineer , you'll shape the technical strategy and delivery of production ML systems that transform raw sports data and live video into real-time insights and personalised experiences for millions of fans. What … technical lead for a critical ML domain (e.g., live sports insights and personalisation , real-time ranking, computer vision for multi-angle video, or streaming inference). Expect to influence roadmaps, architecture, and platform evolution-not just single models-while mentoring engineers and data scientists and raising the bar across teams. ...

predictive insights during the action. As a Lead Machine Learning Engineer , you'll shape the technical strategy and delivery of production ML systems that transform raw sports data and live video into real-time insights and personalised experiences for millions of fans. What … technical lead for a critical ML domain (e.g., live sports insights and personalisation , real-time ranking, computer vision for multi-angle video, or streaming inference). Expect to influence roadmaps, architecture, and platform evolution-not just single models-while mentoring engineers and data scientists and raising the bar across teams. ...

predictive insights during the action. As a Lead Machine Learning Engineer , you'll shape the technical strategy and delivery of production ML systems that transform raw sports data and live video into real-time insights and personalised experiences for millions of fans. What … technical lead for a critical ML domain (e.g., live sports insights and personalisation , real-time ranking, computer vision for multi-angle video, or streaming inference). Expect to influence roadmaps, architecture, and platform evolution-not just single models-while mentoring engineers and data scientists and raising the bar across teams. ...

limit and you'll help us get there. Key Responsibilities Stay up to date with the state-of-the-art in Artificial Intelligence and Machine Learning, and apply it to the problem of Enterprise Vibe Coding; Make use of techniques such as In-Context Learning (Prompt Engineering … practice in a large-scale project. Qualifications/What You Need To Succeed Ph.D. or MSc degree in Computer Science, Electrical Engineering, Artificial Intelligence, Machine Learning, or related technical fields is highly valued. 7+ years of work experience in Machine Learning and AI Engineering roles, preferably ...

will be equally comfortable working with internal teams or external customers and partners. Some knowledge delivering software projects with elements of Autonomy or Machine Learning would be advantageous but is not necessary. You can expect to be working as part of a world renowned, expert engineering team, with … experience that demonstrates the ability to step-up to this level of responsibility. Prior experience delivering software involving technology related to Autonomous Systems, or Machine Learning would be advantageous but not necessary. Similarly, experience delivering defence related software projects would be helpful, but is not a requirement. ...

predictive insights during the action. As a Lead Machine Learning Engineer , you'll shape the technical strategy and delivery of production ML systems that transform raw sports data and live video into real-time insights and personalised experiences for millions of fans. What … technical lead for a critical ML domain (e.g., live sports insights and personalisation , real-time ranking, computer vision for multi-angle video, or streaming inference). Expect to influence roadmaps, architecture, and platform evolution-not just single models-while mentoring engineers and data scientists and raising the bar across teams. ...

predictive insights during the action. As a Lead Machine Learning Engineer , you'll shape the technical strategy and delivery of production ML systems that transform raw sports data and live video into real-time insights and personalised experiences for millions of fans. What … technical lead for a critical ML domain (e.g., live sports insights and personalisation , real-time ranking, computer vision for multi-angle video, or streaming inference). Expect to influence roadmaps, architecture, and platform evolution-not just single models-while mentoring engineers and data scientists and raising the bar across teams. ...

predictive insights during the action. As a Lead Machine Learning Engineer , you'll shape the technical strategy and delivery of production ML systems that transform raw sports data and live video into real-time insights and personalised experiences for millions of fans. What … technical lead for a critical ML domain (e.g., live sports insights and personalisation , real-time ranking, computer vision for multi-angle video, or streaming inference). Expect to influence roadmaps, architecture, and platform evolution-not just single models-while mentoring engineers and data scientists and raising the bar across teams. ...