Job
Description
Design, develop, and implement MLOps pipelines for the continuous deployment and integration of machine learning models. Collaborate with data scientists and engineers to understand model requirements and optimize deployment processes. Take offline models data scientists build and turn them into a real machine learning production system. Automate the training, testing and deployment processes for machine learning models. Continuously monitor and maintain models in production, ensuring optimal performance, accuracy and reliability. Implement best practices for version control, model reproducibility and governance. Optimize machine learning pipelines for scalability, efficiency and cost-effectiveness. Troubleshoot and resolve issues related to model deployment and performance. Ensure compliance with security and data privacy standards in all MLOps activities. Keep up-to-date with the latest MLOps tools, technologies and trends. Provide support and guidance to other team members on MLOps practices. Communicate with a team of data scientists, data engineers and architect, document the processe Experience in designing and implementing pipelines MLOps on AWS, Azure, or GCP. Hands on building CI/CD pipelines orchestration using TeamCity, Jenkins, Airflow or similar tools. Experience with MLOps Frameworks like Kubeflow, MLFlow, DataRobot, Airflow etc., experience with Docker and Kubernetes, OpenShift. Programming languages like Python, Go, Ruby or Bash, good understanding of Linux, knowledge of frameworks such as scikit-learn, Keras, PyTorch, Tensorflow, etc. Ability to understand tools used by data scientist and experience with software development and test automation. Fluent in English, good communication skills and ability to work in a team.
