Data Scientist - ML Engineering
Tasks
- Build trust and credibility
- Deploy ML at enterprise scale
- Deploy high-scale ML systems in hybrid/multi-cloud environments
- Identify workflow streamlining opportunities
- Lead ML platform or DevOps teams
- Lead complex client conversations
- Leverage AI tools for workflow optimization
- Monitor and audit ML systems
- Translate technical topics into business impact
- Utilize AutoML and H2O
- Work with feature stores and feature engineering
Perks/Benefits
- Flexible culture
- Global opportunities
- High-impact environment
- Professional development
- Total rewards
- Vibrant community
Skills/Tech-stack
AI Tooling | Automl | Azure Databricks | Cloud infrastructure | Docker | Feature Engineering | H2O | Kubernetes | ML deployment | MLflow | Monitoring | Multi-cloud | Multi-cloud infrastructure | Spark
Education
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