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.
Perks and Benefits for Irisians
Iris provides world-class benefits for a personalized employee experience. These benefits are designed to support financial, health and well-being needs of Irisians for a holistic professional and personal growth. Click to view the benefits.
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