Build and own the infrastructure and pipelines used to train, evaluate, package, deploy, and
Develop reliable MLOps capabilities across experiment tracking, model and data versioning
Partner with Data Science, Engineering, and Infrastructure teams to productionize models and

🎯 Requirements

5-7+ years of experience in Machine Learning Engineering, MLOps, ML Infrastructure, Platform
Strong hands-on experience with Python, cloud infrastructure, Docker, Kubernetes, CI/CD
Proven experience building and operating production ML systems, including training pipelines
Experience managing GPU-based training workloads and/or distributed training infrastructure, and
Experience working in a fast-growing startup, with the ability to operate in a dynamic, fast-paced

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