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 data loading 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 (Client) and Big Data platforms in Hybrid and multi-cloud environment, specifically in AWS SageMaker environment
Experience in the container, streaming, and messaging technologies is a plus
Qualification:
Bachelor's degree in computer science, data science, mathematics, or a related field. Master's degree preferred
At least 2 years experience as a machine learning engineer.
Advanced proficiency with Python framework, Java, and Scala
Extensive knowledge of Client frameworks, libraries, data structures, data modeling, and software architecture.
Good understanding of mathematics, statistics, and algorithms.
Excellent analytical and problem-solving abilities.