• Looking for Mlops Engineer
Responsibilities• Build MLOps pipelines to support development, experimentation, continuous integration, continuous delivery, verification/validation, and monitoring of AI/ML models • ML Orchestration needs to integrate seamlessly with the DevOps practices already in place in Enterprise data platform. • Implement required logging capability to monitoring of Machine learning models in production. • Collaborate with the data engineers and data scientists on feature development in order to containerise and build out the deployment pipelines for new modules • Automate applications and infrastructure deployments. • Design and promote the adoption of an automated strategy for data versioning for modelling experiments and in production. • Model development framework enabling accelerated development and release into production for any model. • Develop standard interfaces of monitoring to ensure models deployed are well maintained, performing as expected and not having any adverse effects on the business. • Scheduled automated retraining based on monitoring of data drifting
Requirements• Data Scientist with 6+ Years of experience in ML Ops • Worked extensively in AL/ML system provisioning & solution deployments • Strong in Python, R. • Experienced in Azure DevOps. • Experience with Docker, Kubernetes clusters and • implementing CI/CD or DevOps / MLOps solutions.
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