About
Define and manage the long-term ML infrastructure roadmap and best practices for model lifecycle management Design, build, and maintain production-grade model deployment and inference systems Collaborate with cross-functional teams to support end-to-end ML workflows and improve system reliability
Required Qualifications
4+ years of experience in ML Ops, ML infrastructure, or backend engineering supporting production ML systems Experience in cloud-native environments (AWS and/or GCP) with ML workload deployment Proven track record in designing and implementing CI/CD pipelines for ML systems Strong programming experience in Python and familiarity with infrastructure-as-code tools like Terraform Hands-on experience with ML lifecycle tooling and managing GPU-based workloads
Languages
- English
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