Responsible for implementing robust data pipeline using Azure Stack
Responsible for creating reusable and scalable data pipeline
Responsible for using Databrick to build model in Datalake/Azure Synapse
Work with the leadership to set the standards for software engineering practices within the machine learning engineering team and support across other disciplines
Data engineers' provision and set up data platform/Streaming data technologies that are on-premises and in the cloud.
Manage and secure the flow of structured and unstructured data from multiple sources.
The data platforms include relational databases, nonrelational databases, data streams, and file stores

Experience with Databricks, data modeling, and Power BI
Knowledge of Big Data technologies, such as Spark, Hadoop/MapReduce is desirable but not essential
Knowledge of Azure services like HD Insight, Azure DataBricks etc.
Working knowledge of the pros, cons and usages of various ML/DL applications (such as Keras, Tensorflow, Python scikit learn and R)
Contribution to industry/open source communities.
Knowledge and practical experience of cloud-based platforms and their ML/DL offerings (such as Google GCP, AWS, and Azure) would be advantageous
Understanding of infrastructure (including hosting, container-based deployments and storage architectures) would be advantageous
Knowledge on Linux, Iseries, Window