ML OPS - Senior Engineer

• 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.

• Demonstrates strong ownership while driving MLOps excellence and operational effectiveness.

• Collaborate effectively with various teams and business stakeholders to ensure smooth delivery.

• Promotes automation-first engineering through proactive optimization and continuous improvement.

• Applies strong analytical thinking to evaluate complex ML deployment and lifecycle management challenges.

• Demonstrates adaptability while managing evolving MLOps technologies and business requirements.

• Communicates effectively regarding deployment status, risks, dependencies, and improvement opportunities.

• Maintains high attention to detail across pipeline design, deployment automation, validation, and operational processes.

• Encourages continuous improvement in MLOps practices, deployment frameworks, and lifecycle management processes.

• Supports knowledge sharing and mentoring to strengthen team capabilities.

• Balances scalability, reliability, governance, and business priorities while driving delivery excellence.