Description

Job Responsibilities include:

Design and maintain scalable Lakehouse architecture and data pipelines using Kafka, Delta Lake/Iceberg, and streaming frameworks (Flink/Spark).

Develop and deploy machine learning models for anomaly detection, root-cause analysis, and incident forecasting across observability domains.

Build real-time streaming inference pipelines for low-latency anomaly scoring and alert generation.

Manage ML lifecycle including feature engineering, model monitoring, drift detection, and automated retraining (MLOps).

Optimize data performance, ensure data quality governance, and enforce schema standards for large-scale observability datasets.

Collaborate with cross-functional teams to integrate AIOps insights with incident management systems and improve operational efficiency.

Required Qualifications:

Bachelor’s or master’s degree in computer science, Data Engineering, Machine Learning, or a related field.

10+ years of experience in data engineering, machine learning, and AIOps/observability platforms.

Strong expertise in Kafka, streaming frameworks (Flink/Spark), and Lakehouse architectures (Delta Lake/Iceberg) with hands-on large-scale data pipeline development.

Proven experience in building and deploying anomaly detection and time-series ML models with solid MLOps practices including feature stores, monitoring, and automated retraining

Key Skills

Education

Any Graduate