About the Role
Design and operate production-grade ML systems that are reliable, scalable, and fully observable. Own the transition of models from experimentation to high-throughput services. Prevent model decay and reduce Data Science operational load through automation and monitoring.
Qualifications- 5+ years in Data Science/ML with 3+ years in MLOps or Production Engineering
- Proven experience deploying and maintaining production ML services
- Strong Python and SQL expertise
- Experience working with cloud platforms (AWS, Azure, or GCP)
- Background integrating ML with business systems (ERP, CRM, Supply Chain)
- Experience collaborating with Data Engineering and DevOps teams
- Ability to build resilient systems in legacy or imperfect environments
- Architect and deploy ML models across the full lifecycle
- Implement monitoring for data drift, model drift, and perfor...
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