NumPy, Scikit-learn, TensorFlow, PyTorch). Hands-on experience implementing end-to-end ML pipelines (data ingestion, training, validation, serving). Familiarity with ML workflow orchestration tools (Airflow, Prefect, Kubeflow) and feature/data platforms (Databricks, Tecton, etc.). Strong experience with cloud platforms (AWS, GCP, or Azure), Docker, and Kubernetes. Solid coding practices, including Git, automated testing, and CI More ❯
NumPy, Scikit-learn, TensorFlow, PyTorch). Hands-on experience implementing end-to-end ML pipelines (data ingestion, training, validation, serving). Familiarity with ML workflow orchestration tools (Airflow, Prefect, Kubeflow) and feature/data platforms (Databricks, Tecton, etc.). Strong experience with cloud platforms (AWS, GCP, or Azure), Docker, and Kubernetes. Solid coding practices, including Git, automated testing, and CI More ❯
london, south east england, united kingdom Hybrid / WFH Options
microTECH Global LTD
monitoring, logging, and performance testing for GPU/ML workloads. Excellent collaboration skills — able to work with research, engineering, and product teams. Desirables: Experience in MLOps/LLMOps (MLflow, Kubeflow, Weights & Biases, experiment tracking). Exposure to video processing, compression, or neural rendering pipelines. Knowledge of FPGA/embedded deployment Contributions to open-source GPU/ML/DevOps projects. More ❯
slough, south east england, united kingdom Hybrid / WFH Options
microTECH Global LTD
monitoring, logging, and performance testing for GPU/ML workloads. Excellent collaboration skills — able to work with research, engineering, and product teams. Desirables: Experience in MLOps/LLMOps (MLflow, Kubeflow, Weights & Biases, experiment tracking). Exposure to video processing, compression, or neural rendering pipelines. Knowledge of FPGA/embedded deployment Contributions to open-source GPU/ML/DevOps projects. More ❯
and Expertise You will have relevant academic (research Masters level) and/or industry experience. Excellent knowledge and experience of managing an on-premise Kubenetes cluster. Excellent knowledge of Kubeflow and similar systems, e.g. MLflow Good programming ability in Python with familiarity with Linux systems including scripting and system configuration. Experience using AWS, e.g, Cognito, S3, EC2, Lamdas, etc. Experience More ❯
/B testing Experiment design and hypothesis testing MLOps & Engineering Scalable ML systems (batch and real-time) ML pipelines, CI/CD, monitoring, deployment Familiarity with tools like MLflow, Kubeflow, Airflow, Docker, Kubernetes Strategic skills Align ML initiatives with business goals Prioritize projects based on ROI, feasibility, and risk Understand market trends and competitive ML strategies Communicate ML impact to More ❯
organisational, ethical, and regulatory requirements Required Expertise: Extensive ML Ops/ML Engineering experience, particularly in assurance, governance, and monitoring Hands-on knowledge of ML Ops platforms and tools (Kubeflow, MLflow, SageMaker, Azure ML, or equivalent) Proven ability to deploy and maintain AI solutions in regulated or complex operational settings Strong grounding in responsible AI practices, including explainability and fairness More ❯
