Role: Machine Learning Engineer/ Architect with AWS
Location: Newark, New Jersey / Remote
- 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.
- Partner with data scientists to understand, implement, train, and design machine learning models.
- Collaborate with the infrastructure team to improve the architecture, scalability, stability, and performance of ML platform.
- Develop processes, model monitoring, and governance framework for successful ML model operationalization.
- 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
Skills / Qualifications:
Bachelor's degree in computer science, computer engineering or any other relevant field of engineering- Masters degree preferred
At least Three years' experience as a machine learning engineer.
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.
Talent Acquisition Synechron