SQL br - Expert knowledge of ML Ops frameworks in the following categories: br a) experiment tracking and model metadata management (e.g. MLflow) br b) orchestration of ML workflows (e.g. Metaflow) br c) data and pipeline versioning (e.g. Data Version Control) br d) model deployment, serving and monitoring (e.g. Kubeflow)/p p br - Expert knowledge of automated artefact deployment using More ❯
SQL br - Expert knowledge of ML Ops frameworks in the following categories: br a) experiment tracking and model metadata management (e.g. MLflow) br b) orchestration of ML workflows (e.g. Metaflow) br c) data and pipeline versioning (e.g. Data Version Control) br d) model deployment, serving and monitoring (e.g. Kubeflow)/p p br - Expert knowledge of automated artefact deployment using More ❯
SQL br - Expert knowledge of ML Ops frameworks in the following categories: br a) experiment tracking and model metadata management (e.g. MLflow) br b) orchestration of ML workflows (e.g. Metaflow) br c) data and pipeline versioning (e.g. Data Version Control) br d) model deployment, serving and monitoring (e.g. Kubeflow)/p p br - Expert knowledge of automated artefact deployment using More ❯
SQL br - Expert knowledge of ML Ops frameworks in the following categories: br a) experiment tracking and model metadata management (e.g. MLflow) br b) orchestration of ML workflows (e.g. Metaflow) br c) data and pipeline versioning (e.g. Data Version Control) br d) model deployment, serving and monitoring (e.g. Kubeflow)/p p br - Expert knowledge of automated artefact deployment using More ❯
SQL br - Expert knowledge of ML Ops frameworks in the following categories: br a) experiment tracking and model metadata management (e.g. MLflow) br b) orchestration of ML workflows (e.g. Metaflow) br c) data and pipeline versioning (e.g. Data Version Control) br d) model deployment, serving and monitoring (e.g. Kubeflow)/p p br - Expert knowledge of automated artefact deployment using More ❯
SQL br - Expert knowledge of ML Ops frameworks in the following categories: br a) experiment tracking and model metadata management (e.g. MLflow) br b) orchestration of ML workflows (e.g. Metaflow) br c) data and pipeline versioning (e.g. Data Version Control) br d) model deployment, serving and monitoring (e.g. Kubeflow)/p p br - Expert knowledge of automated artefact deployment using More ❯
SQL br - Expert knowledge of ML Ops frameworks in the following categories: br a) experiment tracking and model metadata management (e.g. MLflow) br b) orchestration of ML workflows (e.g. Metaflow) br c) data and pipeline versioning (e.g. Data Version Control) br d) model deployment, serving and monitoring (e.g. Kubeflow)/p p br - Expert knowledge of automated artefact deployment using More ❯
SQL br - Expert knowledge of ML Ops frameworks in the following categories: br a) experiment tracking and model metadata management (e.g. MLflow) br b) orchestration of ML workflows (e.g. Metaflow) br c) data and pipeline versioning (e.g. Data Version Control) br d) model deployment, serving and monitoring (e.g. Kubeflow)/p p br - Expert knowledge of automated artefact deployment using More ❯
SQL br - Expert knowledge of ML Ops frameworks in the following categories: br a) experiment tracking and model metadata management (e.g. MLflow) br b) orchestration of ML workflows (e.g. Metaflow) br c) data and pipeline versioning (e.g. Data Version Control) br d) model deployment, serving and monitoring (e.g. Kubeflow)/p p br - Expert knowledge of automated artefact deployment using More ❯
SQL br - Expert knowledge of ML Ops frameworks in the following categories: br a) experiment tracking and model metadata management (e.g. MLflow) br b) orchestration of ML workflows (e.g. Metaflow) br c) data and pipeline versioning (e.g. Data Version Control) br d) model deployment, serving and monitoring (e.g. Kubeflow)/p p br - Expert knowledge of automated artefact deployment using More ❯
SQL br - Expert knowledge of ML Ops frameworks in the following categories: br a) experiment tracking and model metadata management (e.g. MLflow) br b) orchestration of ML workflows (e.g. Metaflow) br c) data and pipeline versioning (e.g. Data Version Control) br d) model deployment, serving and monitoring (e.g. Kubeflow)/p p br - Expert knowledge of automated artefact deployment using More ❯
SQL br - Expert knowledge of ML Ops frameworks in the following categories: br a) experiment tracking and model metadata management (e.g. MLflow) br b) orchestration of ML workflows (e.g. Metaflow) br c) data and pipeline versioning (e.g. Data Version Control) br d) model deployment, serving and monitoring (e.g. Kubeflow)/p p br - Expert knowledge of automated artefact deployment using More ❯
SQL br - Expert knowledge of ML Ops frameworks in the following categories: br a) experiment tracking and model metadata management (e.g. MLflow) br b) orchestration of ML workflows (e.g. Metaflow) br c) data and pipeline versioning (e.g. Data Version Control) br d) model deployment, serving and monitoring (e.g. Kubeflow)/p p br - Expert knowledge of automated artefact deployment using More ❯
SQL br - Expert knowledge of ML Ops frameworks in the following categories: br a) experiment tracking and model metadata management (e.g. MLflow) br b) orchestration of ML workflows (e.g. Metaflow) br c) data and pipeline versioning (e.g. Data Version Control) br d) model deployment, serving and monitoring (e.g. Kubeflow)/p p br - Expert knowledge of automated artefact deployment using More ❯
SQL br - Expert knowledge of ML Ops frameworks in the following categories: br a) experiment tracking and model metadata management (e.g. MLflow) br b) orchestration of ML workflows (e.g. Metaflow) br c) data and pipeline versioning (e.g. Data Version Control) br d) model deployment, serving and monitoring (e.g. Kubeflow)/p p br - Expert knowledge of automated artefact deployment using More ❯
SQL br - Expert knowledge of ML Ops frameworks in the following categories: br a) experiment tracking and model metadata management (e.g. MLflow) br b) orchestration of ML workflows (e.g. Metaflow) br c) data and pipeline versioning (e.g. Data Version Control) br d) model deployment, serving and monitoring (e.g. Kubeflow)/p p br - Expert knowledge of automated artefact deployment using More ❯
Newcastle upon Tyne, Northumberland, United Kingdom
SQL br - Expert knowledge of ML Ops frameworks in the following categories: br a) experiment tracking and model metadata management (e.g. MLflow) br b) orchestration of ML workflows (e.g. Metaflow) br c) data and pipeline versioning (e.g. Data Version Control) br d) model deployment, serving and monitoring (e.g. Kubeflow)/p p br - Expert knowledge of automated artefact deployment using More ❯
Kingston upon Hull, East Yorkshire, United Kingdom
SQL br - Expert knowledge of ML Ops frameworks in the following categories: br a) experiment tracking and model metadata management (e.g. MLflow) br b) orchestration of ML workflows (e.g. Metaflow) br c) data and pipeline versioning (e.g. Data Version Control) br d) model deployment, serving and monitoring (e.g. Kubeflow)/p p br - Expert knowledge of automated artefact deployment using More ❯
Python and ML/engineering frameworks such as PyTorch, TensorFlow (including Keras), Hugging Face (Transformers, Datasets) and scikit-learn, etc Experience with MLOps tools, including MLFlow, workflow orchestrators (Airflow, Metaflow, Perfect or similar), and containerisation (Docker) Strong knowledge of cloud platforms like Azure, AWS or GCP for deploying and managing ML models Familiarity with data engineering tools and practices, e.g. More ❯
London, England, United Kingdom Hybrid / WFH Options
Artefact
/testing datasets, cross-validation, performance visualisation, and use hosted APIs; explore techniques like time-series forecasting, clustering, or Bayesian inference. Orchestration and Parallelisation : Manage workflows with tools like Metaflow, MLFlow, AirFlow, or DVC; utilise parallelisation frameworks like PySpark or Ray for efficient model processing. Exposure to cloud platforms (AWS, Azure, GCP) Why you should join us Artefact is revolutionizing More ❯
datasets suitable for machine learning research and production. High-Performance Data Pipelines Develop and optimize distributed systems for data processing, including filtering, indexing, and retrieval, leveraging frameworks like Ray, Metaflow, Spark, or Hadoop. Synthetic Data Generation Build and orchestrate pipelines to generate synthetic data at scale, advancing research on cost-efficient inference and training strategies. Experiments & Analysis Design and conduct More ❯
London, England, United Kingdom Hybrid / WFH Options
Bupa
difference – and who brings the following: Strong Python coding skills. Proficiency with Azure and orchestration tools like Azure DevOps (ADO). Experience with MLOps frameworks such as MLflow, Kubeflow, Metaflow, or SageMaker. Experience deploying real-time inference services and batch prediction pipelines. Solid understanding of CI/CD practices and monitoring tools (e.g., Prometheus, Grafana). Benefits Our benefits are More ❯
team on a 9-month engagement. Key requirements for this role include: Proficiency in Python programming Experience in writing and implementing code Testing and quality assurance skills Knowledge of Metaflow & Prefect Experience working with Raster Data This is a 9-month contract outside IR35, paying £450 per day. The client is based in Bath, and you will be expected to More ❯
