1 to 25 of 186 Remote MLOps Jobs in the UK

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, and code version control. Skilled in data pipeline creation and working with both More ❯

SQL, NoSQL, and big data technologies (Spark, Hadoop, Snowflake, Databricks, etc.). Strong background in statistical modelling, probability theory, and mathematical optimization. Experience deploying machine learning models to production (MLOps, Docker, Kubernetes, etc.). Familiarity with AWS/GCP/Azure cloud ML platforms for scalable model training and inference. Strong problem-solving, communication, and business acumen skills. Experience in More ❯

and ability to communicate complex ideas clearly. Desirable Skills Experience with deep learning architectures (CNNs, RNNs, Transformers). Familiarity with cloud platforms (AWS, GCP, or Azure). Exposure to MLOps tools or pipeline automation. Previous internship or research experience in a related field. What’s on Offer Competitive graduate salary and benefits package. A collaborative, learning-focused environment. Opportunity to More ❯

LLM orchestration frameworks, and prompt tuning. Familiarity with RAG pipelines, embedding models, and vector databases. Experience with cloud platforms (AWS, GCP, Azure) and AI/ML services. Knowledge of MLOps tools and practices (e.g., MLflow, Kubeflow, Vertex AI, Azure ML). Strong understanding of data engineering, data pipelines, and ETL workflows. Excellent problem-solving, communication, and stakeholder engagement skills. Bachelor More ❯

LLM orchestration frameworks, and prompt tuning. Familiarity with RAG pipelines, embedding models, and vector databases. Experience with cloud platforms (AWS, GCP, Azure) and AI/ML services. Knowledge of MLOps tools and practices (e.g., MLflow, Kubeflow, Vertex AI, Azure ML). Strong understanding of data engineering, data pipelines, and ETL workflows. Excellent problem-solving, communication, and stakeholder engagement skills. Bachelor More ❯

LLM orchestration frameworks, and prompt tuning. Familiarity with RAG pipelines, embedding models, and vector databases. Experience with cloud platforms (AWS, GCP, Azure) and AI/ML services. Knowledge of MLOps tools and practices (e.g., MLflow, Kubeflow, Vertex AI, Azure ML). Strong understanding of data engineering, data pipelines, and ETL workflows. Excellent problem-solving, communication, and stakeholder engagement skills. Bachelor More ❯

real-world tradeoffs. Fluency in Python and commonly used ML libraries (e.g. pandas, scikit-learn; experience with PyTorch or TensorFlow is a plus). Experience with model lifecycle management (MLOps), including monitoring, retraining, and model versioning. Ability to work across data infrastructure, from SQL to large-scale distributed data tools (Spark, etc.). Strong written and verbal communication skills, especially More ❯

real-world tradeoffs. Fluency in Python and commonly used ML libraries (e.g. pandas, scikit-learn; experience with PyTorch or TensorFlow is a plus). Experience with model lifecycle management (MLOps), including monitoring, retraining, and model versioning. Ability to work across data infrastructure, from SQL to large-scale distributed data tools (Spark, etc.). Strong written and verbal communication skills, especially 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 ❯

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 ❯

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 ❯

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 ❯

Career Growth: Direct mentorship from senior AI engineers and founders, clear progression in a high-growth AI compliance company 🛠 Modern Tech Stack on AWS: Work with cutting-edge LLMs, MLOps tooling, Python, TypeScript, and an AWS-native cloud stack 🤝 Collaborative Culture: Join a supportive, mission-driven team passionate about safe and responsible AI ✨ Additional Perks: Pension scheme, private healthcare, regular More ❯

Career Growth: Direct mentorship from senior AI engineers and founders, clear progression in a high-growth AI compliance company 🛠 Modern Tech Stack on AWS: Work with cutting-edge LLMs, MLOps tooling, Python, TypeScript, and an AWS-native cloud stack 🤝 Collaborative Culture: Join a supportive, mission-driven team passionate about safe and responsible AI ✨ Additional Perks: Pension scheme, private healthcare, regular More ❯

marketing performance managers. Experience in Python and SQL. Experience with cloud platforms such as Amazon Web Services, Azure, or Google Cloud Platform. Deep understanding of Generative AI, NLP, and MLOps concepts. Analytical, flexible, independent, and a strong communicator. WE OFFER: A flexible, hybrid working policy (2 days from the office, depending on location). An excellent salary based on experience More ❯

marketing performance managers. Experience in Python and SQL. Experience with cloud platforms such as Amazon Web Services, Azure, or Google Cloud Platform. Deep understanding of Generative AI, NLP, and MLOps concepts. Analytical, flexible, independent, and a strong communicator. WE OFFER: A flexible, hybrid working policy (2 days from the office, depending on location). An excellent salary based on experience More ❯

based data platforms (AWS, Azure, or GCP). Proven track record in credit risk modelling, fraud analytics, or similar financial domains. Familiarity with big data technologies (Spark, Hive) and MLOps practices for production-scale deployments. Excellent communication skills to engage stakeholders and simplify complex concepts. Desirable Extras Experience with regulatory frameworks (e.g., Basel, GDPR) and model explainability tools. Knowledge of More ❯

based data platforms (AWS, Azure, or GCP). Proven track record in credit risk modelling, fraud analytics, or similar financial domains. Familiarity with big data technologies (Spark, Hive) and MLOps practices for production-scale deployments. Excellent communication skills to engage stakeholders and simplify complex concepts. Desirable Extras Experience with regulatory frameworks (e.g., Basel, GDPR) and model explainability tools. Knowledge of More ❯

Computer Science, Data Science, Engineering, Mathematics, etc.). Strong proficiency in Python Hands-on experience with LLMs , prompt design , RAG architectures , or fine-tuning foundation models . Understanding of MLOps practices (e.g., Docker, FastAPI, MLflow, cloud-based model deployment). Strong grasp of NLP techniques and evaluation methods for generative models. Excellent analytical and communication skills, with a focus on More ❯

Factory, GCP Dataflow and AWS services. Apply data modelling concepts (e.g., medallion architecture) and ensure data quality and governance. Collaborate with DevOps teams to automate CI/CD and MLOps processes using Azure DevOps, Kubernetes, and Terraform. Visualize and communicate insights using Power BI, Tableau, and PowerApps. Mentor junior engineers and contribute to internal knowledge sharing. Ensure solutions meet security More ❯

that drives engagement, conversion, and growth. Oversee the design, training, and deployment of search and recommendation models — from data strategy to monitoring and performance optimisation. Collaborate with platform and MLOps teams to ensure robust, efficient, and scalable ML workflows (including CI/CD, feature management, and monitoring). Share insights and best practices across other ML teams, particularly in areas More ❯