Systems Engineer

• Design and build scalable ML infrastructure including model serving systems, feature stores, and training pipelines
• Develop and maintain robust data pipelines for ML workflows, from data ingestion to model deployment
• Implement MLOps best practices including CI/CD for ML models, monitoring, and automated retraining pipelines
• Build and optimize model serving infrastructure to support real-time and batch inference at scale
• Collaborate with ML engineers and data scientists to understand infrastructure requirements and translate them into reliable systems
• Monitor and optimize ML system performance, reliability, and cost efficiency
• Establish infrastructure standards and tooling to accelerate ML development workflows
• Own the deployment and operational aspects of ML models in production

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