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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📌 Ml Ops - Senior Engineer (Noida)
🏢 IRIS SOFTWARE
📍 Noida