26 to 50 of 436 MLOps Jobs in England

ability to engage senior stakeholders Nice to Have: Experience in consulting or client-facing delivery roles Knowledge of cloud platforms (AWS, GCP, Azure) and MLOps best practices Advanced degree (MSc or PhD) in a quantitative discipline Why Join Us? High-impact work with world-class clients Competitive salary and bonus More ❯

other relevant programming languages. Experience with ML frameworks like TensorFlow, PyTorch, and Scikit-learn. Hands-on experience with Microsoft Azure AI services. Knowledge of MLOps best practices. Experience with Azure data and analytics services such as Data Factory, Data Lake, and Synapse. Experience designing AI solutions, including virtual assistants and More ❯

CI/CD for microservices or ML pipelines. Solid understanding of networking, Linux, and security fundamentals. Preferred Qualifications Experience supporting GPU‑accelerated workloads or MLOps/LLMOps platforms. Familiarity with service mesh (Istio, Linkerd) and event‑driven architectures (Kafka, Pub/Sub). Knowledge of distributed storage systems (Ceph, MinIO More ❯

CI/CD for microservices or ML pipelines. Solid understanding of networking, Linux, and security fundamentals. Preferred Qualifications Experience supporting GPU‑accelerated workloads or MLOps/LLMOps platforms. Familiarity with service mesh (Istio, Linkerd) and event‑driven architectures (Kafka, Pub/Sub). Knowledge of distributed storage systems (Ceph, MinIO More ❯

for complex property management workflows and decision-making processes Hands-on experience with AI/ML frameworks (TensorFlow, PyTorch), LLM orchestration tools (LangChain, LangGraph), MLOps practices and tooling (such as MLflow, Kubeflow, or similar), vector databases, and cloud platforms (AWS, Azure, GCP) with their AI/ML offerings Preferably hands More ❯

are well on our way-but there's still an exciting journey ahead. Join us at the heart of trust. As part of the MLOps team, you'll work closely with data scientists, software engineers, and other stakeholders to bring machine learning models to life-ensuring they're deployed, maintained More ❯

business objectives. Preferred: AI Deployment Experience: Hands-on experience with DevOps methodologies tailored for AI, including CI/CD pipelines for model deployment and MLOps practices. AI-Driven Automation: Previous experience working with AI automation engines, process automation using AI, or integrating AI with business logic to streamline processes. Enterprise More ❯

agile working environment, with a focus on iterative and collaborative project delivery. Have worked with data visualisation tools e.g. Tableau. Experience in model monitoring. (MLOPS experience would be desirable.) Have gained a master's in a quantitative field such as Computer Science, Statistics or related disciplines. Recruitment Process: Pre-screen More ❯

an agile working environment, with a focus on iterative and collaborative project delivery. Experience with data visualisation tools e.g. Tableau. Experience in model monitoring. (MLOPS experience would be desirable.) Gained a master's in a quantitative field such as Computer Science, Statistics, or related disciplines. Saga Values: Make it Happen More ❯

PyTorch, Hugging Face Transformers, Pandas, and scikit-learn. Strong understanding of cloud services (preferably AWS) and experience with Docker, Kubernetes, MLFlow, Kubeflow, or similar MLOps tools. 📩 Interested? Apply below or email me at mmatysik@trg-uk.com. More ❯

PyTorch, Hugging Face Transformers, Pandas, and scikit-learn. Strong understanding of cloud services (preferably AWS) and experience with Docker, Kubernetes, MLFlow, Kubeflow, or similar MLOps tools. 📩 Interested? Apply below or email me at mmatysik@trg-uk.com. More ❯

Java. Experience scaling machine learning on data and compute grids. Proficiency with Kubernetes, Docker, Linux, and cloud computing. Experience with Dask, Airflow, and MLflow. MLOps, CI, Git, and Agile processes. Why you do not want to miss this career opportunity? We are a mission-driven firm that is revolutionising the More ❯

sources, 3rd party providers and publicly available data. Technology & Infrastructure Advisory: Assess and recommend data science tooling, platform architecture (cloud/on‑premise), and MLOps practices to ensure scalable, maintainable analytics pipelines (including API calls to various services) What You’ll Bring: Extensive experience in data strategy and analytics consulting More ❯

help define it as it evolves. Nice to Have Experience working with ML models and their deployment. Familiarity with ML infrastructure, feature stores, and MLOps best practices. Exposure to deep learning frameworks (PyTorch, TensorFlow). Experience with building internal tools, dashboards, or lightweight front-end components (e.g., Streamlit, Dash, or More ❯

sources, 3rd party providers and publicly available data. Technology & Infrastructure Advisory: Assess and recommend data science tooling, platform architecture (cloud/on‑premise), and MLOps practices to ensure scalable, maintainable analytics pipelines (including API calls to various services) What You’ll Bring: Extensive experience in data strategy and analytics consulting More ❯

function & procedure) and Snowpark is a plus Experience with unit and integration tests Strong understanding of machine learning algorithms and best practices Vision for MLOps best practices, particularly regarding version control, Docker, MLFlow, CI/CD Strong communication skills, with the ability to engage effectively with diverse stakeholders Good commercial More ❯

. Experience applying probabilistic models in real-world applications (e.g., recommendation systems, anomaly detection, personalized healthcare, etc.). Understanding of modern ML pipelines and MLOps (e.g., MLFlow, Weights & Biases). Experience with recent trends such as foundation models , causal inference , or RL with uncertainty . Track record of publishing or More ❯

Experience with debugging ML models. Experience with orchestration frameworks (e.g. Airflow, MLFlow, etc). Experience deploying machine learning models to production environments. Knowledge of MLOps practices and tools for model monitoring and maintenance. Familiarity with containerization and orchestration tools like Docker and Kubernetes. Hands-on experience with cloud platforms such More ❯

. Skills we'd also like to hear about: Evidence of modelling experience applied to industry relevant use cases. Familiarity with working in an MLOps environment. Familiarity with simulation techniques. Familiarity with optimisation techniques. What you'll receive from us: No matter where you may be in your career or More ❯

. Skills we'd also like to hear about: Evidence of modelling experience applied to industry relevant use cases. Familiarity with working in an MLOps environment. Familiarity with simulation techniques. Familiarity with optimisation techniques. What you'll receive from us: No matter where you may be in your career or More ❯

. Skills we'd also like to hear about: Evidence of modelling experience applied to industry relevant use cases. Familiarity with working in an MLOps environment. Familiarity with simulation techniques. Familiarity with optimisation techniques. What you'll receive from us: No matter where you may be in your career or More ❯

. Skills we'd also like to hear about: Evidence of modelling experience applied to industry relevant use cases. Familiarity with working in an MLOps environment. Familiarity with simulation techniques. Familiarity with optimisation techniques. What you'll receive from us: No matter where you may be in your career or More ❯

numerate degree (e.g. Maths, Stats, Engineering, Computer Science). Desirable: Experience using Databricks or working in a cloud-based environment like Azure. Exposure to MLOps, version control, or productionising models. Experience working with Jira and Confluence in an Agile environment is advantageous. Streamlit, Power BI or other standard BI software More ❯

Python/other AI-related languages. Strong SQL and data analytics skills. Familiarity with cloud platforms (AWS and Azure) for AI deployment. Knowledge of MLOps principles for scaling AI models. Understanding of knowledge graphs, semantic search, and vector databases. AI Ethics and Responsible AI Awareness of AI ethics, bias mitigation More ❯

Scala, C# or Java, cloud SDKs and APIs. AI/ML expertise for pipeline efficiency, familiar with TensorFlow, PyTorch, AutoML, Python/R, and MLOps (MLflow, Kubeflow). Solid in DevOps, CI/CD automation with Bitbucket Pipelines, Azure DevOps, GitHub. Automate deployment of data pipelines and applications using Bash More ❯