Ml Ops - Senior Engineer

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 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.

Mandatory Competencies

Data AI - MLOPS - CI/CD (for ML pipelines)

Data AI - MLOPS - Data Pipeline Feature Management

Data AI - MLOPS - Model Deployment Serving

Data AI - MLOPS - Model Lifecycle Management

Data AI - MLOPS - Model Registry Experiment Tracking

Data AI - MLOPS - Monitoring Observation

Data AI - MLOPS - Python

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