ML Infrastructure Engineer
Key responsibilities:
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Design and build scalable machine learning infrastructure
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Develop data pipelines and modern big data processing systems
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Design and build an elastic infrastructure for continuous integration and continuous deployment (CI/CD)
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Select and configure data storage for efficient ML data management
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Implement MLOps/DevOps best practices for deployment and monitoring of services
Qualifications:
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Bachelor’s degree in Computer Science, Computer Engineering, or related field
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2+ years of professional experience with Amazon Web Services or other cloud providers
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Experience designing and implementing web and cloud services
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Experience with modern MLOps tools, such as MLflow, Kubeflow, Airflow, Neptune, Optuna, DataRobot
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Experience with streaming data and event-driven systems and tools such as Kinesis, Kafka, Flink, Spark
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Experience with cloud native software delivery and infrastructure as code
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Passionate about Machine Learning infrastructure
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Authorized to work lawfully in the United States
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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”.
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