MLOPs Engineer

Key Responsibilities:

• MLOps Pipeline Development: Design, build, and maintain end-to-end MLOps pipelines for deploying ML and GenAI models.
• Automation: Automate model training, validation, deployment, and monitoring using industry best practices.
• CI/CD Optimization: Optimize CI/CD workflows for ML models and integrate them with cloud and on-premises infrastructure.
• Model Serving: Develop scalable model serving solutions using frameworks like TensorFlow Serving, Triton Inference Server, or similar tools.
• Versioning & Testing: Implement model versioning, A/B testing, and rollback strategies to ensure reliable model updates.
• Collaboration: Work closely with Data Scientists to operationalize ML models and ensure reproducibility.
• Monitoring & Observability: Monitor model performance, drift, and infrastructure health using ML observability tools.