We are seeking an experienced MLOps Engineer with robust expertise in ML lifecycle management, Kubernetes, CI/CD, ML platform engineering, model deployment, monitoring, and cloud infrastructure. The ideal candidate will have hands-on experience operationalizing machine learning models, building scalable ML pipelines, and implementing production-grade MLOps solutions across AWS, Azure, or GCP settings.
Key Responsibilities
Design and implement end-to-end MLOps workflows covering training, deployment, monitoring, retraining, and governance.
Build scalable ML pipelines and automate model lifecycle processes.
Deploy and manage ML models on Kubernetes-based platforms.
Implement CI/CD pipelines for machine learning solutions.
Develop experiment tracking, model versioning, and model registry frameworks.
Monitor model performance, detect drift, and automate retraining workflows.
Build dashboards, alerting mechanisms, and observability solutions.
Collaborate with Data Scientists and Engineers to operationalize ML solutions.
Implement Infrastructure as Code and cloud-native MLOps best practices.