26 to 50 of 417 MLOps Jobs in the UK

for predominantly time-series forecasting Collaborate with data scientists and researchers to productionise models Manage cloud-based and on-prem setup Requirements: 25 years experience in ML engineering/MLOps roles Strong proficiency in Python and experience with relevant ML libraries/frameworks (pandas, numpy, sklearn, TensorFlow, PyTorch) Strong understanding and experience in implementing end-to-end ML pipelines (data More ❯

documented Python SDKs and APIs. Partner with customers to architect and deploy solutions across AWS/GCP/Azure and containerized environments, guiding them through troubleshooting too. Advise on MLOps, model deployment patterns, and data workflow integration. Build and maintain technical documentation, tutorials, code samples, and reference architectures. Gather customer feedback and translate it into clear product requirements. Collaborate closely More ❯

documented Python SDKs and APIs. Partner with customers to architect and deploy solutions across AWS/GCP/Azure and containerized environments, guiding them through troubleshooting too. Advise on MLOps, model deployment patterns, and data workflow integration. Build and maintain technical documentation, tutorials, code samples, and reference architectures. Gather customer feedback and translate it into clear product requirements. Collaborate closely More ❯

algorithms based on project requirements. Evaluate models using appropriate metrics and perform hyperparameter tuning for optimal performance. Convert proof-of-concept models into production-grade pipelines in collaboration with MLOps and engineering teams. Required: Translate model outcomes into actionable insights through clear storytelling and visualizations. Build dashboards and reports using Power BI, Tableau, or Python-based visualization tools. Communicate findings More ❯

platforms. Experience managing developer platforms, tools, and services. Hands on experience with DevOps and engineering tools (GitLab, GitHub, Azure DevOps, Docker, Kubernetes). Proficiency with AI/ML and MLOps platforms (Databricks, Google Cloud Vertex AI, SageMaker). Familiarity with generative AI technologies and frameworks (OpenAI, Google Gemini, Hugging Face Transformers). Demonstrated success in developing and executing product strategies. More ❯

Engineer Machine Learning Engineer – AI for Advanced Materials – Oxford (Tech stack: Python, PyTorch, TensorFlow, Scikit-learn, MLflow, Airflow, Docker, Kubernetes, AWS, Azure, GCP, Pandas, NumPy, SciPy, CI/CD, MLOps, Data Visualization, Bayesian Modelling, Probabilistic Programming, Terraform) We’re looking for a Machine Learning Engineer to join a rapidly scaling deep-tech company that’s reinventing how the world designs More ❯

Engineer - AI for Advanced Materials - Oxford/Remote (UK) (Tech stack: Python, PyTorch, TensorFlow, Scikit-learn, MLflow, Airflow, Docker, Kubernetes, AWS, Azure, GCP, Pandas, NumPy, SciPy, CI/CD, MLOps, Data Visualization, Bayesian Modelling, Probabilistic Programming, Terraform) We're looking for a Machine Learning Engineer to join a rapidly scaling deep-tech company that's reinventing how the world designs More ❯

the team Advanced, hands-on expertise in SQL and big data platformslike Databricks, used for sophisticated data manipulation, feature engineering, and optimizing complex data workflows Extensive, proven experience in MLOps: owning the end-to-end lifecycle of production models , including designing scalable and reliable deployment strategies (e.g., serverless applications, containerization) and model monitoring, observability and continuous improvement Strong proficiency in More ❯

with Front end Frameworks such as Angular, for integration of AI-powered applications Experience with graph databases and knowledge graphs (Neo4j) for knowledge graphs and tool routing. Cloud deployment & MLOps Production deployments on Azure (AKS/ACI/Functions), CI/CD, and Infrastructure as Code (Bicep/Terraform). Data & Information Management Experience with relational/semi-structured database More ❯

with cloud platforms like Microsoft Azure or Google Cloud Platform (GCP); familiarity with Databricks is a plus. Comfort working in cloud-based or secure environments, with an interest in MLOps or model deployment. Awareness and practice of responsible AI, data ethics, and governance frameworks. Willingness to explore emerging technologies such as agentic AI, GitHub Copilot, Microsoft Copilot and Copilot Studio. More ❯

Job Title: MLOps Engineer Location: Bristol – 1-day onsite p/m Start Date: ASAP Salary: £75,000 - £90,000 Note: Candidates must be willing and eligible to undergo SC Clearance. Join a European leader in AI-driven technology, delivering innovative surveillance solutions that enhance safety and save lives. They provide cutting-edge services combining real-time intelligence, data, and … AI to transform operational outcomes globally. About the Role We are seeking an MLOps Engineer to manage and optimise the machine learning lifecycle, from development to deployment and monitoring. You will work closely with data scientists, software engineers, and IT teams to ensure seamless, scalable, and reliable machine learning solutions in production environments. Key Responsibilities - Pipeline Development: Build and maintain … deliver solutions that meet technical and business requirements. - Security & Governance: Implement best practices for data security, model governance, and compliance. - Documentation & Innovation: Maintain clear documentation and stay updated on MLOps trends and technologies. Profile & Requirements - Bachelor’s or Master’s in Computer Science, Data Science, Engineering, or related field. - 3+ years in ML, DevOps, or MLOps with hands-on experience More ❯

CI/CD pipelines, and GitHub Actions. Knowledge of containerization (tools ie Docker) and orchestration (ie Kubernetes on Azure). Good awareness of Data & AI - understanding of ML lifecycle, MLOps, and Responsible AI. Strong problem-solving and analytical skills. Excellent communication and stakeholder management skills. Nice to have: Familiarity with LLM fine-tuning. Strong academic background in IT/Computer More ❯

CI/CD pipelines, and GitHub Actions. Knowledge of containerization (tools ie Docker) and orchestration (ie Kubernetes on Azure). Good awareness of Data & AI - understanding of ML lifecycle, MLOps, and Responsible AI. Strong problem-solving and analytical skills. Excellent communication and stakeholder management skills. Nice to have: Familiarity with LLM fine-tuning. Strong academic background in IT/Computer More ❯

R), SQL, and experience with notebooks, Git workflows, and Power BI. Working knowledge of supervised machine learning (e.g., gradient boosting, logistic regression), evaluation metrics, and experiment design. Exposure to MLOps concepts, cloud platforms (e.g., Azure), and GenAI tools is a strong plus. Structured thinking, strong problem-solving, and clear communication skills. Degree (2:1 or equivalent) in a numerical discipline More ❯

products Ensure best practices in data engineering and contribute to architectural decisions Support senior team members in identifying and addressing data science opportunities. Required Skills & Experience Proven experience in MLOps or DevOps roles within machine learning environments Strong programming skills in Python, with hands-on experience in PySpark and SQL Deep understanding of ML lifecycle management and CI/CD More ❯

pragmatic mindset — able to balance innovation with execution. ✨ Nice-to-haves: Experience leading platform or DevOps product teams in life sciences, research, or AI-driven organisations . Familiarity with MLOps, GenAI, and large-scale compute orchestration . Background in computer science, engineering, or a related technical discipline. 💰 Package & Benefits: Base salary up to £145,000 . Car allowance , 18% bonus More ❯

pragmatic mindset — able to balance innovation with execution. ✨ Nice-to-haves: Experience leading platform or DevOps product teams in life sciences, research, or AI-driven organisations . Familiarity with MLOps, GenAI, and large-scale compute orchestration . Background in computer science, engineering, or a related technical discipline. 💰 Package & Benefits: Base salary up to £145,000 . Car allowance , 18% bonus More ❯

pragmatic mindset - able to balance innovation with execution. Nice-to-haves: Experience leading platform or DevOps product teams in life sciences, research, or AI-driven organisations . Familiarity with MLOps, GenAI, and large-scale compute orchestration . Background in computer science, engineering, or a related technical discipline. Package & Benefits: Base salary up to £145,000 . Car allowance , 18% bonus More ❯

that drive real-world outcomes for complex, high-stakes environments. This is a fast-paced, technically elite environment, ideal for someone who thrives on solving operational challenges, building robust MLOps infrastructure, and leading the delivery of AI systems at scale. The Role As a Senior Machine Learning Engineer, you’ll be part of cross-functional delivery teams working on technically More ❯

that drive real-world outcomes for complex, high-stakes environments. This is a fast-paced, technically elite environment, ideal for someone who thrives on solving operational challenges, building robust MLOps infrastructure, and leading the delivery of AI systems at scale. The Role As a Senior Machine Learning Engineer, you’ll be part of cross-functional delivery teams working on technically More ❯

R), SQL, and experience with notebooks, Git workflows, and Power BI. Working knowledge of supervised machine learning (e.g., gradient boosting, logistic regression), evaluation metrics, and experiment design. Exposure to MLOps concepts, cloud platforms (e.g., Azure), and GenAI tools is a strong plus. Structured thinking, strong problem-solving, and clear communication skills. Degree (2:1 or equivalent) in a numerical discipline More ❯

machine learning frameworks (TensorFlow, PyTorch, Scikit-learn) Experience with LLM integration (OpenAI, Anthropic, open-source models) Knowledge of RAG architectures, prompt engineering, and vector databases (Pinecone, Weaviate) Experience with MLOps tools and monitoring model performance in production Automation Architecture Deep knowledge of automation tools including GitHub Actions, Terraform, and Ansible Experience with business process automation (RPA) tools like Appian Workflow More ❯

FastAPI. Cloud Platform: Mastery of GCP, particularly Vertex AI, Google Kubernetes Engine (GKE), and Cloud Functions. Databases: Strong command of relational databases like PostgreSQL and familiarity with NoSQL solutions. MLOps & DevOps: Production experience with Docker, Kubernetes, CI/CD pipelines (e.g., Jenkins, GitHub Actions), and Infrastructure as Code (Terraform). Qualifications Bachelor's or Master's degree in Computer Science More ❯

FastAPI. Cloud Platform: Mastery of GCP, particularly Vertex AI, Google Kubernetes Engine (GKE), and Cloud Functions. Databases: Strong command of relational databases like PostgreSQL and familiarity with NoSQL solutions. MLOps & DevOps: Production experience with Docker, Kubernetes, CI/CD pipelines (e.g., Jenkins, GitHub Actions), and Infrastructure as Code (Terraform). Qualifications Bachelor's or Master's degree in Computer Science More ❯

machine learning frameworks (TensorFlow, PyTorch, Scikit-learn) Experience with LLM integration (OpenAI, Anthropic, open-source models) Knowledge of RAG architectures, prompt engineering, and vector databases (Pinecone, Weaviate) Experience with MLOps tools and monitoring model performance in production Automation Architecture Deep knowledge of automation tools including GitHub Actions, Terraform, and Ansible Experience with business process automation (RPA) tools like Appian Workflow More ❯