Mandatory Skills:

Machine Learning (ML), CI/CD (for ML pipelines), Data Pipeline Feature Management, Model Deployment Serving, Model Lifecycle Management, Model Registry Experiment Tracking, Monitoring Observation, Python

Key Responsibilities
Design and implement scalable CI/CD frameworks for machine learning lifecycle management and deployment automation.
Define model deployment architectures and serving strategies aligned with business and operational requirements.
Lead implementation of automated ML pipeline solutions supporting model validation, testing, release, and deployment processes.
Design and optimize model serving frameworks to improve scalability, reliability, and operational efficiency.
Establish model registry standards for model versioning, governance, traceability, and lifecycle management.
Define experiment tracking frameworks to support reproducibility, auditability, and model performance management.
Design and implement model monitoring frameworks to evaluate prediction quality, model performance, data drift, concept drift,



and operational health while supporting proactive model lifecycle management and retraining strategies.
Establish deployment validation and model quality assurance practices to improve production readiness.
Review ML pipeline designs and deployment implementations to ensure adherence to engineering and operational standards.
Troubleshoot complex deployment, serving, and ML lifecycle management challenges through detailed root cause analysis.
Mentor team members on MLOps practices, deployment automation, model lifecycle management, and operational excellence.
Collaborate with various teams and stakeholders to support end-to-end ML platform delivery.
Drive continuous improvement initiatives focused on automation, reliability, governance, and operational efficiency.

Behavioral Competencies
Demonstrates solid ownership while driving MLOps excellence and operational effectiveness.
Collaborate effec

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