- Own, operate, and improve backend services running in Azure, including serverless services, batch workloads, and ML inference endpoints.
- Manage deployments and reliability across environments, including CI/CD, monitoring, alerting, incident response, and operational runbooks.
- Design and build ETL/ELT pipelines that transform data from relational and document databases into analysis-ready datasets.
- Build and maintain infrastructure-as-code and manage cloud infrastructure, access controls, secrets, and costs.
- Implement monitoring, logging, data-quality checks, and freshness alerting across data workflows.
- Build infrastructure and tooling for ML and Operations Research experimentation, deployment, evaluation, and reproducibility.
- Package, version, and deploy models, and build automated evaluation and benchmarking pipelines.
- Establish practices for code quality, automated testing, version control, and CI/CD; conduct peer code reviews.
- Collaborate with Data Science, Operations Research, Product, and Engineering to integrate ML and optimization solutions.
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