Position:

ML Engineer

:

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 workplace.

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 prospects in model monitoring, alerting, and retraining workflows.

Location:

IN-MH-Pune, India-Blue Ridge-Hinjewadi (eInfochips)

Time Type:

Full time

Job Category:

Engineering Services

📌 Ml Engineer Pune
🏢 Arrow Ec
📍 Pune