Principal ML Infrastructure Engineer
UpworkFull Time
Expert & Leadership (9+ years)
The ideal candidate will have 5+ years of experience in infrastructure/platform engineering or large-scale distributed systems, with at least 2 years of hands-on experience using SQL-based cloud data warehouses such as BigQuery, Snowflake, Redshift, or Databricks. Proficiency in large-scale feature computation frameworks like Spark, PySpark, or Scala, and expertise in distributed systems, including scaling, partitioning, fault tolerance, and caching are essential. Familiarity with ML production systems, particularly ML feature platforms, and knowledge of MLOps workflows are highly desirable.
The Senior Software Engineer will be responsible for designing and building data infrastructure to support large-scale feature computation, transformation, and storage. They will develop frameworks for batch and event-driven features, focusing on reliability, scalability, and ease of use. Key duties include driving improvements in data quality and governance through validation, anomaly detection, drift monitoring, and feature lineage tracking. The engineer will partner with ML engineers for seamless integration of feature engineering workflows into ML production systems, contribute to training set generation pipelines, and ensure reproducibility and feature versioning for model development. Additionally, they will help shape the future of the platform by exploring streaming feature management and other next-generation capabilities.
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