ML/Cloud based system that efficiently analyzes collected data to predict/prevent/troubleshoot system failures and performance issues in smart-devices
Multi-tenancy & medium-high data volume processing
Data collected from smart devices is accessed from cloud (AWS) storage and undergoes translation from device-specific schema, file formats, etc and transformations such as selection of relevant data and features before being applied to a ML model training subsystem; qualified models are then pushed to production environment for prediction/execution. Data handling employs scalable spark-based access. The entire processing workflow is kept in sync via pipelines defined in airflow.
The state of the entire data engineering (& ML models, training and execution) is available via Dashboard UI
• Bring Spark Big Data expertise to a local Scrum team, providing qualified deliverables and services on schedule.
• Participate in progress reviews
• Work with scrum master(s), tech lead(s) to analyze and understand user stories in each sprint
• Complete coding & unit testing for the allotted stories
• Create design documents or make changes to existing ones
• Complete code reviews
Mandatory Skills:Apache Spark, Java, Spring Boot, Microservices
Nice-to-Have Skills:Experience with AWS
English: B2 Upper Intermediate
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