Description

You will own the development and deployment of ML models for observability use cases.

Responsibilities

• Develop and deploy machine learning models for anomaly detection, predictive alerting, incident forecasting, and automated root cause analysis.
• Build and optimize ML models using Python libraries to identify behavioral patterns and predict system failures across Azure ecosystems.
• Architect scalable, real-time data pipelines using Apache Kafka to ingest high-volume observability data.
• Utilize Azure Databricks for large-scale data processing, feature engineering, and automating MLOps pipelines.
• Establish complete MLOps workflows, managing model versioning, CI/CD, and performance monitoring to reduce MTTD and MTTR.

Required Skills

• 5+ years of experience in MLOps, Data Engineering, or Data Science.
• Strong knowledge of machine learning applied to time-series data and AIOps use cases.
• Hands-on experience with Kafka, Flink, and Azure Databricks.
• Proficiency in Python and ML libraries including TensorFlow, PyTorch, and Scikit-learn.
• Experience understanding observability ecosystems like Datadog, Prometheus, or Elastic.
• Proven experience deploying ML models in production and managing the full model lifecycle.

Key Skills

Education

Bachelor's degree