426 to 450 of 898 MLOps Jobs in the UK

This job is brought to you by Jobs/Redefined, the UK's leading over-50s age inclusive jobs board. Who are we? Our mission is to scale intelligence to serve humanity. We're training and deploying frontier models for More ❯

We believe that we are better together, and at Tripadvisor we welcome you for who you are. Our workplace is for everyone, as is our people powered platform. At Tripadvisor, we want you to bring your unique perspective and experiences More ❯

Senior Machine Learning & MLOps Engineer 💰 £110,000-£120,000 + up to 10% bonus 🖥️ MLOps, Python, Terraform 🌍 London, one day a week in office 🦄 UK tech unicorn We are partnered with one of the UK's most profitable and exciting Insurtech companies who have reached a valuation of over $1BN. This will be their first ML/MLOps Engineering hire … warehouses like Snowflake. Version control: You are proficient with git. If you are interested in finding out more, please apply or contact me directly! 📧 jordanf@burnssheehan.co.uk Senior Machine Learning & MLOps Engineer 💰 £110,000-£120,000 + up to 10% bonus 🖥️ MLOps, Python, Terraform 🌍 London, one day a week in office 🦄 UK tech unicorn More ❯

Senior Machine Learning & MLOps Engineer 💰 £110,000-£120,000 + up to 10% bonus 🖥️ MLOps, Python, Terraform 🌍 London, one day a week in office 🦄 UK tech unicorn We are partnered with one of the UK's most profitable and exciting Insurtech companies who have reached a valuation of over $1BN. This will be their first ML/MLOps Engineering hire … warehouses like Snowflake. Version control: You are proficient with git. If you are interested in finding out more, please apply or contact me directly! 📧 jordanf@burnssheehan.co.uk Senior Machine Learning & MLOps Engineer 💰 £110,000-£120,000 + up to 10% bonus 🖥️ MLOps, Python, Terraform 🌍 London, one day a week in office 🦄 UK tech unicorn More ❯

AI and data science. Define and align product roadmaps with customer needs and market trends. Evaluate and integrate emerging AI technologies to enhance product capabilities. Build and maintain robust MLOps pipelines for scalable and reliable AI solutions. Collaborate with cross-functional teams to translate data insights into business value. Mentor and lead a team of data scientists and product developers. … learning, or data science roles. Proven track record of developing and launching successful data-driven products, ideally within a SaaS or enterprise software environment. Experience in building and maintaining MLOps pipelines and working with cloud computing platforms (e.g., AWS, Azure, GCP). Familiarity with ERP systems and the discrete manufacturing industry is preferred. Experience in leading and mentoring teams of … Python, R, or Java. Deep understanding of machine learning, deep learning, and other AI techniques. Experience with data engineering tools and frameworks. Strong knowledge of cloud computing platforms and MLOps practices. About This Job's Location While the job advertisement specifies the UK as the preferred location, we encourage talented individuals from around the globe to apply. This position can More ❯

cutting-edge machine learning solutions that influence decision-making across a large, fast-moving business. You'll have access to vast datasets, modern tooling, and the support of experienced MLOps and Data Engineering teams – freeing you to focus on model innovation , business impact , and team leadership . Key Responsibilities Lead and coach a team of data scientists focused on pricing … senior stakeholders to prioritise and deliver key initiatives. Work cross-functionally with Marketing and Operations data teams to extend the reach of data science across the organisation. Collaborate with MLOps and Engineering teams to ensure seamless product delivery and integration. Promote the use and value of pricing models to non-technical stakeholders through clear and effective communication. Continuously improve the … flexible hybrid work model (Reading or London) and a collaborative environment. A role where your models directly shape pricing, influence profitability, and deliver real commercial outcomes. Support from seasoned MLOps and engineering teams – letting you focus on research, modelling, and innovation . If you’re passionate about pricing science and ready to step into a leadership role where your work More ❯

cutting-edge machine learning solutions that influence decision-making across a large, fast-moving business. You'll have access to vast datasets, modern tooling, and the support of experienced MLOps and Data Engineering teams – freeing you to focus on model innovation , business impact , and team leadership . Key Responsibilities Lead and coach a team of data scientists focused on pricing … senior stakeholders to prioritise and deliver key initiatives. Work cross-functionally with Marketing and Operations data teams to extend the reach of data science across the organisation. Collaborate with MLOps and Engineering teams to ensure seamless product delivery and integration. Promote the use and value of pricing models to non-technical stakeholders through clear and effective communication. Continuously improve the … flexible hybrid work model (Reading or London) and a collaborative environment. A role where your models directly shape pricing, influence profitability, and deliver real commercial outcomes. Support from seasoned MLOps and engineering teams – letting you focus on research, modelling, and innovation . If you’re passionate about pricing science and ready to step into a leadership role where your work More ❯

and data science solutions for a diverse client base. Role Responsibilities Design and implement ML and Generative AI models to address client challenges Work closely with data engineers and MLOps professionals to deliver production-ready systems Apply techniques such as large language models (LLMs), retrieval-augmented generation (RAG), vector databases, prompt engineering, and model fine-tuning Engage with client stakeholders … Generative AI projects from concept to deployment Hands-on experience with frameworks such as Hugging Face, LangChain, and open-source LLMs Familiarity with tools such as Databricks and modern MLOps workflows Strong Python skills and familiarity with common data science tools and libraries Experience with cloud platforms (preferably Azure, but AWS or GCP also valuable) Confident communicating complex technical ideas More ❯

and data science solutions for a diverse client base. Role Responsibilities Design and implement ML and Generative AI models to address client challenges Work closely with data engineers and MLOps professionals to deliver production-ready systems Apply techniques such as large language models (LLMs), retrieval-augmented generation (RAG), vector databases, prompt engineering, and model fine-tuning Engage with client stakeholders … Generative AI projects from concept to deployment Hands-on experience with frameworks such as Hugging Face, LangChain, and open-source LLMs Familiarity with tools such as Databricks and modern MLOps workflows Strong Python skills and familiarity with common data science tools and libraries Experience with cloud platforms (preferably Azure, but AWS or GCP also valuable) Confident communicating complex technical ideas More ❯

to-service communication, API gateways , and data management . AI & Machine Learning Platform: AI is a critical component of the product. You will lead the product vision for our MLOps pipelines and the integration of both traditional and generative AI models into our platform. DevOps & Automation: You will be responsible for the product strategy that underpins engineering enablement. This includes … AWS , and system architecture design. Deep understanding of microservices , Docker , security best practices, DevOps principles, CI/CD , and system monitoring. Direct experience with AI/ML products, including MLOps and the productization of machine learning models (both traditional and generative). Proven ability to bridge business and technical perspectives to make informed trade-offs between speed, quality, and scalability. More ❯

to-service communication, API gateways , and data management . AI & Machine Learning Platform: AI is a critical component of the product. You will lead the product vision for our MLOps pipelines and the integration of both traditional and generative AI models into our platform. DevOps & Automation: You will be responsible for the product strategy that underpins engineering enablement. This includes … AWS , and system architecture design. Deep understanding of microservices , Docker , security best practices, DevOps principles, CI/CD , and system monitoring. Direct experience with AI/ML products, including MLOps and the productization of machine learning models (both traditional and generative). Proven ability to bridge business and technical perspectives to make informed trade-offs between speed, quality, and scalability. More ❯

Azure ML Studio and Azure DevOps. Effective Communication: Present findings clearly through visualisation and reporting, including interactive dashboards and web applications using Power BI, R Shiny, or Python Flask. MLOps Maintenance: Maintain and enhance MLOps pipelines, focusing on model robustness and performance. Project Tracking: Manage code and project tracking using Git and Azure DevOps Boards, aligned with Scaled Agile Framework … a candidate with a blend of essential skills and experience, including: Hands-On Experience: Proven experience in data science and machine learning model deployment, preferably with Azure ML Studio. MLOps Proficiency: Strong understanding of pipeline management and model versioning. Data Manipulation Skills: Expertise in data extraction, transformation, and reshaping using SQL, R/Tidyverse, and Python/Pandas. visualisation Expertise More ❯

core AI platform capabilities, supporting a Machine Learning Engineer on classical ML projects, and building the foundation for a future Data Science function. You will also define LLMOps and MLOps practices and ensure the infrastructure is in place to support long-term AI success across the business. Key Responsibilities: Strategy & Leadership Collaborate over the AI roadmap across GenAI, ML, and … lifecycle automation. Maintain awareness of emerging models and integration strategies. Machine Learning Engineering: Support the ML Engineer in model development, deployment, and monitoring. Contribute to pipelines, experimentation design, and MLOps enablement. Provide code and architectural reviews, as well as technical mentoring. Data Science Enablement Act as Data Scientist to support analytics and decision-making needs. Develop experimentation frameworks, metric definitions … and deploying GenAI applications in production. Strong hands-on knowledge of LLMs, prompt engineering, and retrieval-augmented generation (RAG). Practical experience with traditional ML, including data pipelines and MLOps workflows. Working knowledge of statistical modelling and experimentation. Proficiency in Python and at least one additional general-purpose language. Strong understanding of cloud-native architectures (AWS preferred). Experience leading More ❯

things ML. Required Skills and Experience: Master’s Degree in Computer Science, Machine Learning, Mathematics, or a related field, with a strong focus on NLP or ML. Proficiency in MLOps best practices, including model versioning, CI/CD pipelines, containerization, and cloud deployment for large-scale models. Solid programming skills in Python and familiarity with machine learning frameworks like TensorFlow … PyTorch, Hugging Face Transformers, and MLOps tools (e.g., MLflow, Kubeflow). Strong analytical and problem-solving skills, with an aptitude for translating complex theoretical research into practical applications. Day to Day Conduct research and implementation on the development, training, and deployment of large language models, with a willingness to work on both pre-training and post-training (fine-tuning, alignment More ❯

/ML Architecture and delivering results in a fast-paced environment. Experience on creating RAG systems and to build applications using flow and agents based on LLMs. Experience with MLOps and DevOps for data and AI Solutions. Proficiency in AI technologies, event driven and real time data analytics environments, development, and utilization of machine learning algorithms, MLOps, online feature stores More ❯

/ML Architecture and delivering results in a fast-paced environment. Experience on creating RAG systems and to build applications using flow and agents based on LLMs. Experience with MLOps and DevOps for data and AI Solutions. Proficiency in AI technologies, event driven and real time data analytics environments, development, and utilization of machine learning algorithms, MLOps, online feature stores More ❯

pipelines to ensure the quality and robustness of deployed models. Architect, deploy, and maintain cloud infrastructure (AWS, GCP, or Azure) to support both web applications and machine learning operations (MLOps) . Ensure high availability, scalability, and security of deployed systems, meeting the expectations of enterprise customers. Foster engineering best practices around code quality, testing, documentation, and DevOps . Ideal Background … and scaling production-grade AI-powered products . Strong collaborator, with a track record of working closely with AI research, product, and infrastructure teams . Bonus Points: Exposure to MLOps , AI observability , or LLM deployment at scale . Experience with data engineering for large-scale pipelines . Prior background in enterprise SaaS or developer tools . Why Join: AI-native More ❯

pipelines to ensure the quality and robustness of deployed models. Architect, deploy, and maintain cloud infrastructure (AWS, GCP, or Azure) to support both web applications and machine learning operations (MLOps) . Ensure high availability, scalability, and security of deployed systems, meeting the expectations of enterprise customers. Foster engineering best practices around code quality, testing, documentation, and DevOps . Ideal Background … and scaling production-grade AI-powered products . Strong collaborator, with a track record of working closely with AI research, product, and infrastructure teams . Bonus Points: Exposure to MLOps , AI observability , or LLM deployment at scale . Experience with data engineering for large-scale pipelines . Prior background in enterprise SaaS or developer tools . Why Join: AI-native More ❯

research prototypes, then collaborate with cross functional dev pods to turn them into production ready capabilities that delight users. Own the ML stack & direction -Define languages, frameworks, infra, and MLOps practices; the road is unpaved and you have free rein (with the CTO as sparring partner and advisor). Steer buy vs build calls -Evaluate third party APIs (OpenAI, Anthropic … as products, not science projects. Build the AI Centre of Excellence -Hire, mentor, and inspire a small but high performing squad of 3-4 ML/AI engineers and MLOps specialists over the next 12 months. Champion data stewardship -Work with Product & Cloud Ops to leverage usage, booking, and sensor data while staying GDPR compliant as a data processor. Own … direction. WHAT YOU BRING Proven track record shipping customer facing ML/AI features in production at scale. Deep hands on skills across model training, fine tuning, evaluation, and MLOps (CI/CD, feature stores, monitoring). Strong software engineering fundamentals (e.g., Python, Typescript/Node, or similar) and cloud experience (AWS preferred). Ability to set technical vision, yet More ❯

Lead Data Science Engineer Research and implement DS applications in Python Create statistical and machine learning models and integrate them into DS applications Work with and help develop our MLOps pipeline Data engineering of data assets to assist in DS application development Writing production quality code to deploy and run models in a sportsbook Create automatic tests to ensure accuracy … one programming language, ideally Python, with a strong understanding of object-oriented programming principles Understanding of different database technologies, including relational and non-relational databases Experience implementing ML automation, MLOps principles and related tools AWS experience will be considered an asset Experience with typical DevOps flows, such as containerisation (e.g. Docker) and monitoring (e.g. ELK, Grafana) Experience with Kubernetes and More ❯

desirability scoring) and ensure ethical, bias-free automation. 3. Cross-Functional Leadership - Lead a team of Product Managers (Data, Salesforce, Decision Engine) and collaborate with: - Data Science & Engineering: Scale MLOps, model governance, and data quality. - Commercial Teams: Develop pricing models for data-as-a-service (DaaS) offerings. - Legal & Compliance: Embed GDPR, PSD2, and AI ethics into product design. 4. Commercialisation … Deep technical expertise: - Data Platforms: Cloud-native architectures (AWS/Asure), data mesh/fabric. - Salesforce: Financial Services Cloud, CRM integrations, CPQ. - AI/ML: Production-grade model deployment, MLOps, ethical AI frameworks. - Strong commercial mindset: Experience monetising data or SaaS products. Desired - MBA or advanced degree in Data Science, Computer Science, or related field. - Experience in automotive supply chain More ❯

US team. Required Skills and Experience: Master’s Degree in Computer Science, Machine Learning, Mathematics, or a related field, with a strong focus on NLP or ML. Proficiency in MLOps best practices, including model versioning, CI/CD pipelines, containerization, and cloud deployment for large-scale models. Solid programming skills in Python and familiarity with machine learning frameworks like TensorFlow … PyTorch, Hugging Face Transformers, and MLOps tools (e.g., Strong analytical and problem-solving skills, with an aptitude for translating complex theoretical research into practical applications. Day to Day Conduct research and implementation on the development, training, and deployment of large language models, with a willingness to work on both pre-training and post-training (fine-tuning, alignment, optimization) processes. Collaborate More ❯

GitLab: GitLab Pipeline • Ansible and REX: Rex Deployment • Data Science : Prompt Engineering + Modern Testing • Data pipeline development and monitoring • Understanding of AI/ML algorithms and their applications • MLOps frameworks • Knowledge of cloud platforms (Azure ML especially) • Model deployment process Expert: (in addition to proficient skills) o Languages/Framework: Azure Open AI o Data Science: Open AI GPT More ❯

GitLab: GitLab Pipeline • Ansible and REX: Rex Deployment • Data Science : Prompt Engineering + Modern Testing • Data pipeline development and monitoring • Understanding of AI/ML algorithms and their applications • MLOps frameworks • Knowledge of cloud platforms (Azure ML especially) • Model deployment process Expert: (in addition to proficient skills) o Languages/Framework: Azure Open AI o Data Science: Open AI GPT More ❯

and champion new technologies Perception Model Lifecycle Management: Lead the end-to-end development of scalable ML models for critical perception tasks (object detection, tracking, scene understanding, mapping), including MLOps, data pipelines, and optimization for robust onboard deployment Team Leadership & Mentorship: Build, mentor, and lead a high-performing team of machine learning engineers, fostering a culture of innovation, technical excellence … perception algorithms (e.g., object detection, segmentation, tracking, SLAM, multi-sensor fusion) in real-world applications Proficiency in Python and C++ Experience with deep learning frameworks (e.g., PyTorch, TensorFlow) and MLOps tools/platforms Communication, interpersonal, and collaboration skills for working in a dynamic, cross-functional environment Company Statements Equal Opportunity Rivian is an equal opportunity employer and complies with all More ❯