Skills And Tools:
- Automation of infrastructure used by the Data Science team.
- Operationalization of Machine Learning projects (MLOps)
- Providing architectural guidance, data migration, capacity planning, implementation, troubleshooting, monitoring.
- ML/DL frameworks (such as l, Torch, Caffe, Theano).
- Best practices & practical challenges in production-level ML systems.
- Power BI - Spark - Kubernetes - Kubeflow.
- CI/CD techniques, DevOps & Automated deployment pipelines.s
- Bachelor's degree in Computer Science, Engineering, a related technical field or equivalent practical experience.
- Hands-on experience deploying production-level machine learning solutions to the cloud.
- Experience with data pipelines & distributed Machine Learning.
- Experience in Linux administration, Docker, Kubernetes and cloud solutions
- Experience across Microsoft Azure Data Stack, including Data Bricks, Data Lake, Data Factory, Data warehouses, Azure functions & Azure synapse.
- Experience in Data warehousing concepts, ETL/ ELT and reporting/analytic tools and environments.
- Experience across Microsoft Azure AI+ML, Analytics & Security services.
- Knowledge of relational database and ETL practices.
- Knowledge of data structures, algorithms and software design.
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