Description

You will own the end-to-end lifecycle of machine learning operations and production system reliability.

This role is on-site.

Responsibilities

• Build, test, and deploy MLOps pipelines on AWS.
• Design automated workflows for data cleaning, feature engineering, model training, and deployment.
• Manage and monitor production ML systems to ensure performance, reliability, and scalability.
• Troubleshoot issues related to ML model performance, data quality, and infrastructure.
• Maintain documentation for MLOps processes and procedures.

Required Skills

• 5-7 years of experience in MLOps, DevOps, or related roles.
• Proficiency in Python and/or other ML programming languages like R, Java, or Scala.
• Deep knowledge of AWS services including SageMaker, Step Functions, Lambda, and CloudFormation.
• Hands-on experience building and deploying ML models in production using AWS.
• Experience with containerization technologies such as Docker and Kubernetes.
• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• Ability to explain technical concepts to non-technical stakeholders.

Preferred Skills

• Experience collaborating with data scientists and software engineers to deploy models.

Key Skills

Education

Bachelor's or Master's degrees