ML Infrastructure Engineer

Key responsibilities:

• Design and build scalable machine learning infrastructure

• Develop data pipelines and modern big data processing systems

• Design and build an elastic infrastructure for continuous integration and continuous deployment (CI/CD)

• Select and configure data storage for efficient ML data management

• Implement MLOps/DevOps best practices for deployment and monitoring of services

Qualifications:

• Bachelor’s degree in Computer Science, Computer Engineering, or related field

• 2+ years of professional experience with Amazon Web Services or other cloud providers

• Experience designing and implementing web and cloud services

• Experience with modern MLOps tools, such as MLflow, Kubeflow, Airflow, Neptune, Optuna, DataRobot

• Experience with streaming data and event-driven systems and tools such as Kinesis, Kafka, Flink, Spark

• Experience with cloud native software delivery and infrastructure as code

• Passionate about Machine Learning infrastructure

• Authorized to work lawfully in the United States

• Generally able to work Pacific Time zone business hours

*To apply, send your PDF resume to join (at) syntiant (dot) com with the subject line “ML Infrastructure Engineer”.