• Strong computer science fundamentals such as algorithms, data structures, multithreading, object-oriented development, distributed applications, client-server architecture.
• Design and implement Machine learning models and data ingestion pipelines.
• Develop and support a platform that enables data scientists to rapidly develop, train, and experiment with machine learning models.
• Expand and optimize data pipelines, data flow, and collection for cross functional teams.
• Create and maintain optimal data pipeline architecture by assembling large, complex data sets to meet functional and non-functional business requirements.
• Identify and implement internal process improvements including automating manual processes, optimizing data delivery, and redesigning infrastructure for greater scalability.
• Support the building of machine learning, data platforms, and infrastructure required for optimal data extraction, transformations, and loading of data from a wide variety of data sources.
• Work with architecture, data, and design teams to assist with data related technical issues and support data infrastructure needs.
• Implement Machine Learning (ML) and Big Data platforms in Hybrid and multi-cloud environment specifically in AWS SageMaker environment
• Experience in container, streaming and messaging technologies is a plus
• Advanced proficiency with Python framework, Java and Scala
• Extensive knowledge of ML frameworks, libraries, data structures, data modeling, and software architecture.
• Good understanding of mathematics, statistics, and algorithms.
• Excellent analytical and problem-solving abilities.
• Great communication and collaboration skills.