Senior MLOps Engineer

Key Responsibilities:

• ML Model Deployment & Management: Build and maintain robust, scalable pipelines for model deployment, continuous integration (CI), and continuous delivery (CD) in an AWS environment.
• GitHub Actions: Implement and maintain GitHub Actions workflows for automating model training, testing, and deployment pipelines.
• Infrastructure Automation: Leverage AWS services like SageMaker, Lambda, EC2, ECS, EKS, S3, CloudFormation, and CloudWatch to automate and manage the deployment of ML models and workflows.
• Model Monitoring & Scaling: Implement monitoring solutions to track model performance, data drift, and model health. Utilize tools like AWS CloudWatch, Prometheus, and Grafana for real-time monitoring and logging.
• Collaboration: Work alongside ML Engineers to optimize model performance and ensure that ML pipelines are aligned with business goals.
• Version Control & Rollbacks: Set up version control for models, enabling easy rollback and experimentation with model versions in production.
• Security & Compliance: Ensure ML systems are secure and compliant with data privacy regulations, implementing access control, encryption, and auditing best practices within the AWS ecosystem.
• Cost Optimization: Monitor and optimize the infrastructure costs related to AWS resources, ensuring efficient use of cloud resources.
• Continuous Improvement: Work on enhancing CI/CD pipelines, infrastructure, and automation to improve model deployment time, stability, and scalability.

Required Qualifications:

• 5+ years of experience in MLOps, DevOps, or Data Engineering roles.
• Strong experience with AWS services including SageMaker, Lambda, EC2, EKS, S3, and CloudFormation.
• Hands-on experience with GitHub Actions for automating ML workflows, including model training, testing, and deployment pipelines.
• Proficient in containerization technologies like Docker for scalable deployments.