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 opportunities 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