Description

Design and deploy machine learning models for streaming multivariate anomaly detection, root-cause analysis, and incident forecasting across petabyte-scale observability data.

This role is on-site.

Responsibilities

• Design and evolve Lakehouse schemas (Delta Lake / Apache Iceberg) for multi-domain observability data at petabyte scale.
• Build and maintain ingestion pipelines from the OTel Collector through Kafka, ensuring exactly-once semantics and strict schema enforcement.
• Design, train, and deploy ML models for real-time anomaly scoring and root-cause analysis using Flink or Spark Streaming.
• Own the ML feature store, managing feature engineering, versioning, backfill pipelines, and point-in-time correct joins for training datasets.
• Instrument model performance tracking, including drift detection, accuracy monitoring, and automated retraining triggers.

Required Skills

• 5+ years of experience in data engineering or ML engineering.
• Strong proficiency in Python and SQL.
• Hands-on experience with Apache Kafka for streaming data ingestion.
• Experience with Spark or Flink for streaming inference and transformation.
• Practical knowledge of MLOps practices, including feature stores and model lifecycle management.
• Familiarity with Lakehouse architectures (Delta Lake or Apache Iceberg).
• Understanding of observability signals (OTel, APM, Logs) and time-series data patterns.

Key Skills

Education

Any Gradute