We are looking for an MLOps Engineer to bridge the gap between model development and production. Your mission is to build the infrastructure and automated pipelines that allow our AI/ML models to be deployed, monitored, and scaled with high reliability.
In this role, you will:
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Design and implement automated end-to-end pipelines (CI/CD/CT) for machine learning workflows.
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Standardize the way models are packaged, versioned, and deployed (Containerization & Orchestration).
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Build monitoring systems to track model performance, data drift, and system health in production.
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Manage scalable compute and storage resources for training and inference using IaC principles.
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Work closely with Data Scientists to transition experimental code into robust, production-ready microservices.
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Ensure the cost-efficiency and latency optimization of model serving layers.