Position:

Job Description:

Model Monitoring & Validation

• Monitor model performance metrics (accuracy, latency, drift, bias).
• Validate prediction outputs and ensure consistency across environments.
• Support model explainability and debugging of prediction anomalies.
• Ensure data quality alignment between training and inference pipelines.

ML Pipeline Support & Monitoring

• Monitor and support ML pipelines across Azure Machine Learning, Databricks, and related services.
• Track model performance, drift, and pipeline execution failures.
• Identify issues in data ingestion, feature engineering, and model inference workflows.
• Ensure SLA adherence and operational stability in a 24×7 production environment.

Model Support & Fix Implementation

• Analyze and fix issues in Python-based ML workflows, including feature pipelines and model logic.
• Support model retraining, versioning, and deployment fixes.
• Improve model stability, performance, and monitoring coverage.
• Follow change management and deployment processes for production ML fixes.

Incident Management & Troubleshooting

• Troubleshoot failed ML pipelines, model training/inference jobs, and deployment issues.
• Analyze alerts related to model performance degradation, drift, or data inconsistencies.
• Execute resolution steps (job restarts, pipeline re-runs, endpoint fixes) and escalate when needed.
• Perform root cause analysis (RCA) for model failures, prediction anomalies, and pipeline breakdowns.

Collaboration & Continuous Improvement

• Act as escalation support (L2) and guide L1 engineers during ML-related incidents.
• Maintain runbooks for model troubleshooting, monitoring, and recovery procedures.
• Document incidents, model issues, and resolution steps.
• Identify automation opportunities in model monitoring, alerting, and retraining workflows.

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