Found Description
Responsibilities
- Convert data science prototypes into reproducible, production-quality ML services.
- Build and operate large-scale data and feature pipelines using Databricks, Spark, and Snowflake.
- Own the full model lifecycle, including MLflow tracking, CI/CD, automated retraining, and drift monitoring.
- Engineer the activation layer for governed delivery of segments and scores into DSPs, SSPs, and programmatic partners.
- Ensure system scalability, latency, cost-efficiency, and reproducibility within an Azure environment.
- Enforce privacy-by-design principles, including data minimization and encryption.
Requirements
- 8–9 years of hands‑on ML engineering experience shipping and operating production systems at scale.
- Proven experience in the media, advertising, or audience activation industry.
- Proficiency with Databricks (including MLflow and Spark), Snowflake, and Azure (ADLS, Azur...