1 to 25 of 38 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 ...

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 ...

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 ...

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 ...

/ML Engineer (Generative AI/LLMs) Location: Fully Remote (UK-based) The Role We're hiring a Senior Machine Learning Engineer to lead the design and production of Generative AI and Large Language Model (LLM) applications. This role sits at the heart of an AI-focused engineering … semi-autonomous agents using modern agent frameworks and Google AI tooling. Collaborate with product and engineering teams to translate complex business requirements into ML-driven solutions. Monitor, optimise, and continuously improve models in live production environments. Contribute to the architecture and evolution of the AI platform and supporting data infrastructure. ...

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 ...

Reinforcement Learning Specialist — Machine Autonomy Division (R&D Prototypes) — Contract-to-Hire — UK & Germany Preferred — Remote About Vareon Vareon is a systems architecture and engineering company building reliable, deterministic AI for physical systems. We develop transparent, steerable intelligence that can be validated under real-world constraints—latency, safety … offs, results, and next steps clearly to a multidisciplinary team. Required Qualifications Strong practical experience implementing and training reinforcement learning systems using modern ML tooling (e.g., PyTorch/JAX and RL libraries or custom code). Proven ability to take ownership of R&D work: formulate hypotheses, prototype quickly ...

Financial Crime, Data & AI-driven SaaS company helping organisations detect, prevent, and manage financial crime at scale. Our platform combines advanced data, AI and machine learning to deliver market-leading solutions across: Financial Crime Risk Management Anti-Money Laundering (AML) & Counter-Terrorist Financing (CTF) KYC & Risk Assessment Watchlist … from anywhere Competitive base salary + uncapped commission High-growth SaaS business in a mission-critical market Opportunity to sell cutting-edge AI and machine learning technology Clear career progression and leadership opportunities Collaborative, ambitious, and supportive culture ...

engineering quality, automation and innovation. Why candidates choose this role You gain ownership over cloud direction and standards. You join a team that encourages learning, experimentation and the use of new technologies. You work in an environment that values both independence and collaboration. You build platforms that directly support ...

MLOps workflows. · Work across internal products and contract-based venture projects. Requirements · Strong foundational programming skills in Python. · Basic understanding of AI/ML models and how they are trained, deployed, and run. · Familiarity/understanding with version control software (git). · Ability to work in a fast-moving environment ...

Data+ certification and enables you to demonstrate your knowledge in advanced data processing, cleaning, and statistical modelling concepts. You will demonstrate your knowledge of machine learning, industry trends and use of specialised data science applications. You will also apply mathematical and statistical methods appropriately. Step ...

easy-to-use online platform. Step 2: Full-Stack AI Mastery Dive deeper with in-demand technical skills including Python programming, data handling, machine learning, and version control with Git and GitHub. You’ll work on hands-on mini projects that mirror real-world challenges, helping you build ...

easy-to-use online platform. Step 2: Full-Stack AI Mastery Dive deeper with in-demand technical skills including Python programming, data handling, machine learning, and version control with Git and GitHub. You’ll work on hands-on mini projects that mirror real-world challenges, helping you build ...

through to being qualified to work in a junior Data Scientist role. Through the interactive courses, you will gain knowledge in Python, R, Machine Learning, AI , and much more. You will also complete mini projects to gain practical experience and test your skills while you study. Step ...

through to being qualified to work in a junior Data Scientist role. Through the interactive courses, you will gain knowledge in Python, R, Machine Learning, AI , and much more. You will also complete mini projects to gain practical experience and test your skills while you study. Step ...

scalable, secure, reliable, and enterprise-ready. Key Responsibilities Own the full platform architecture, including ingestion, scoring, data stores, APIs, and dashboards. Lead AI/ML-powered enrichment and scoring systems. Build and scale microservices, event-driven pipelines, and automated intelligence workflows. Harden cloud infrastructure and implement security, compliance ...

DevOps engineer and gradually own key infrastructure components Future Growth Shape technical direction and architecture. Mentor engineers as the team scales. Explore AI/ML to enhance the product and internal workflows. What You'll Bring Professional software development experience (typically 3+ years) but open to exceptional candidates with less. ...

Process: Intro call where we talk about the role. Technical discussion with the Head of AI. Deep-dive session with a Backend Engineer and ML Engineer from the team. 30-minute conversation with the Founder. Why This Is Worth Your Time Real ownership: You'll be the architect behind ...

weeks) using Agile methods (Scrum or Kanban) with regular demos and retrospectives. Build and lead cross-functional squads including Product Owner, Data Scientists, ML Engineers, Integration Engineers, Solution Architect, and domain experts from Admissions, Registry and Student Services. Design experiments, test hypotheses, and iterate quickly, ensuring human-in-the-loop … with robust architecture, integration patterns, security controls, SLAs, and operational runbooks. Establish MLOps across the university environment, including model registry, CI/CD for ML, feature stores, monitoring for drift, and retraining policies. Build reusable AI platform capabilities (e.g., orchestration, RAG services, prompt safety, connectors) that support multiple domains such ...