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Establish software engineering best practices for machine learning model deployments, including CI/CD, automation, modularization, automated testing, and monitoring
Develop and deploy scalable tools and services to manage machine learning training, inference, model versioning, model promotion/demotion, and model performance evaluation/tracking
Identify and evaluate new technologies to improve performance, maintainability, scalability and reliability of our production machine learning systems
Administer resource configuration, access, and alerts to ensure appropriate use of computational resources for model development and deployment
Desired Profile:
You have 5+ years experience in applied DevOps for Machine Learning or Data Science teams in industry with a Bachelor’s degree in computer science, machine learning, software engineering or similar field
Experience with model deployment, model maintenance and the methods to evaluate models in product use
Expert in Python and Spark/pySpark
Experience building end-to-end systems as a Platform Engineer, ML Engineer, Data Scientist or Data Engineer
Experience developing and maintaining ML systems built with open source tools
Strong understanding of software testing, benchmarking, and continuous integration
Hands on experience using AWS tools and ElasticSearch
You possess proven experience across a range of DevOps capabilities such as log aggregation, CI/CD, Linux, Docker and Kubernetes
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