- Design, build, and operate scalable backend services, APIs, and data pipelines.
- Improve the reliability, performance, and observability of production ML and optimization systems.
- Own the path from trained model to production: model versioning and registry (MLflow), safe rollout and rollback, and monitoring for data quality and model drift.
- Build clean interfaces that let new ML models and decisioning capabilities integrate safely and efficiently, including experimentation and feature-flag tooling.
- Strengthen engineering foundations across a growing codebase: automated testing, type checking, CI/CD, infrastructure as code, documentation, and thoughtful system design.
- Profile data-heavy services and pipelines; reduce execution time and memory footprint where it matters.
- Collaborate with data scientists, operations researchers, and product engineers to translate business needs into robust technical solutions.
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