Design and maintain scalable ML pipelines for ingestion, training, and deployment.
Build data workflows with Spark, Hadoop, Kafka, Snowflake.
Develop experiment tracking, reporting, and observability.
Provide reusable ML components, SDKs, and APIs for teams.
Drive CI/CD and infrastructure-as-code for ML jobs.
Collaborate with Product, Data Science, and Engineering teams.

🎯 Requirements

3-10 years with petabyte-scale data; BS/MS in CS or Data Eng.
Proficient in SQL and Python (pandas, NumPy) for data exploration.
Experience with Spark, Hadoop, Kafka, and Snowflake data warehousing.
ML Ops/Infrastructure: CI/CD, Docker, Kubernetes, Airflow/MLflow, experiment tracking.
Programming: Python/Scala/Java for data-intensive apps.
Familiar with Private Marketplace ecosystems and deal analytics.

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