MLOps _ ML Engineer

Key Responsibilities :

• Architect and implement scalable MLOps pipelines for model development, deployment, and monitoring
• Lead end-to-end operationalization of ML models using AWS SageMaker and AWS ecosystem
• Build and manage CI / CD pipelines for ML workflows using tools like GitHub Actions, Jenkins, or AWS CodePipeline
• Automate key model lifecycle stages including training, version control, deployment, and rollback
• Collaborate closely with data scientists, ML engineers, and DevOps for seamless model integration
• Monitor live models for performance degradation, data drift, and reliability issues
• Establish governance and best practices for reproducibility, security, and compliance in ML systems

Required Skills :

Preferred Qualifications :

Skills Required

Terraform, Docker, Kubernetes, Prometheus, Python, MLops