- Refactor research-quality and prototype ML code into production-ready, maintainable, deployable systems.
- Own end-to-end deployment of ML pipelines, including data ingestion, feature engineering, model serving, monitoring, and reliability.
- Integrate task management, Git workflows, code reviews, and CI/CD practices.
- Collaborate with researchers and the team to translate research intent into observable production systems.
- Identify architectural and engineering quality issues and implement appropriate solutions.
- Build and maintain monitoring, logging, and alerting infrastructure.
- Support the team's transition toward Rust-native infrastructure.
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