Implement infra to deploy, monitor, and manage ML models in production
Build automated ML pipelines for feature engineering, deployment, and evaluation
Collaborate with data scientists and engineers to operationalize models
Develop CI/CD pipelines for reproducible model releases
Establish monitoring, logging, and alerting for peak model health
Evaluate new tech and best practices to upgrade the engineering stack

🎯 Requirements

3+ years in MLOps, Data Engineering, or core infra
Proficiency in Python backend development
Deploying ML models in distributed environments
Workflow orchestration systems such as Apache Airflow
Distributed data tools like Kafka and Spark
Experience implementing automated CI/CD workflows

🎁 Benefits

Remote environment: work from home with a global team
Self-managed PTO: take time off when you need
Flexible work hours for work-life balance
Culture of innovation and big ideas
MacBook or PC + $500 equipment allowance

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