Senior MLOps Engineer

Introduction:

As a Senior MLOps Engineer, you will be responsible for overseeing the deployment, monitoring, and maintenance of machine learning models in a production environment. You will work closely with data scientists, machine learning engineers, and software developers to ensure seamless integration of machine learning models into operational systems.

Responsibilities:

• Implement and maintain continuous integration and continuous delivery processes for machine learning models
• Utilize tools such as Docker, Kubernetes, and Terraform for containerization and infrastructure as code
• Deploy machine learning models using AWS SageMaker and manage model pipelines
• Ensure model governance and risk management practices are in place
• Collaborate with data integration teams to streamline data pipelines
• Monitor production systems for performance and reliability
• Provide mentorship and training to data scientists on best practices for model deployment
• Conduct testing and rollback procedures as needed

Requirements:

Required Skills:

• Experience with Continuous Delivery and Continuous Integration
• Proficiency in Docker, IBM DB2, Amazon Web Services, Apache Kafka, and Data Integration
• Knowledge of Machine Learning (ML) and Machine Learning Operations (MLOps)
• Familiarity with Amazon ECR, Amazon S3, Python, Risk Management, Training, SQL, and Snow Flake Schema

Preferred Skills:

• Experience with Amazon SageMaker, Kubernetes, and Terraform
• Ability to conduct testing and ensure production monitoring
• Experience in regulated environments