Job Description & Responsibilities

MLOps Strategy & Implementation: Develop and implement MLOps strategies to streamline the deployment, monitoring, and management of machine learning models in production.
Infrastructure Management: Design and maintain scalable infrastructure for deploying machine learning models, leveraging cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes).
Automation: Create automated workflows for model training, validation, deployment, and monitoring, ensuring continuous integration and continuous delivery (CI/CD) for ML models.
Collaboration: Work closely with data scientists to understand model requirements and ensure proper integration with existing systems. Collaborate with DevOps teams to ensure robust and scalable deployments.
Monitoring & Performance: Implement monitoring solutions to track model performance, detect anomalies, and manage model drift. Optimize model inference performance and resource utilization.
Security & Compliance: Ensure the security and compliance of ML models and data pipelines, adhering to best practices and industry standards.
Documentation & Training: Develop comprehensive documentation for MLOps processes and workflows. Provide training and support to team members on MLOps best practices and tools.

Senior MLOps Engineer|Bangalore|6+Years

Job Description & Responsibilities