Ml ops engineer we are seeking a highly skilled ml ops engineer to join our team. as an ml ops engineer, you will be responsible for designing and implementing scalable ml infrastructure on databricks, leveraging unity catalog and feature stores to support model development and deployment. you will also be responsible for detecting data and model drift, ensuring continuous monitoring and high reliability of ml models in production. additionally, you will develop model calibration frameworks and establish versioning practices to maintain transparency and reproducibility across the ml lifecycle. other responsibilities include designing and optimizing reinforcement learning (rl) orchestration pipelines, creating automated frameworks for training, retraining, and validating ml models, and implementing ci/cd best practices to streamline the deployment and monitoring of ml models. required skills and qualifications: - 3+ years of experience in mlops, ml engineering, data engineering, or related roles, focusing on deploying and managing ml workflows in production environments. - 5+ years of experience using python. - proficient in using databricks, apache spark, ml flow, unity catalog, and feature stores. - familiarity with ml lifecycle tools such as mlflow, kubeflow, and airflow. - strong knowledge of git workflows, ci/cd practices, and tools like gitlab or similar. - upper-intermediate english level. benefits: - professional growth: accelerate your professional journey with mentorship, techtalks, and personalized growth roadmaps. - competitive compensation: w...
Ml ops engineer we are seeking a highly skilled ml ops engineer to join our team. as an ml ops engineer, you will be responsible for designing and implementing scalable ml infrastructure on databricks, leveraging unity catalog and feature stores to support model development and deployment. you will also be responsible for detecting data and model drift, ensuring continuous monitoring and high reliability of ml models in production. additionally, you will develop model calibration frameworks and establish versioning practices to maintain transparency and reproducibility across the ml lifecycle. other responsibilities include designing and optimizing reinforcement learning (rl) orchestration pipelines, creating automated frameworks for training, retraining, and validating ml models, and implementing ci/cd best practices to streamline the deployment and monitoring of ml models. required skills and qualifications: - 3+ years of experience in mlops, ml engineering, data engineering, or related roles, focusing on deploying and managing ml workflows in production environments. - 5+ years of experience using python. - proficient in using databricks, apache spark, ml flow, unity catalog, and feature stores. - familiarity with ml lifecycle tools such as mlflow, kubeflow, and airflow. - strong knowledge of git workflows, ci/cd practices, and tools like gitlab or similar. - upper-intermediate english level. benefits: - professional growth: accelerate your professional journey with mentorship, techtalks, and personalized growth roadmaps. - competitive compensation: we match your ...
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