Description

Build and maintain scalable infrastructure for ML model deployment and pipeline orchestration. You will own the release lifecycle from containerization to production monitoring.

Responsibilities

• Build and maintain scalable infrastructure for ML model and pipeline deployment using containerization and orchestration.
• Develop and maintain secure REST APIs to serve multiple machine learning models to various users.
• Design and optimize data pipelines, storage, and processing systems to support training and inference.
• Collaborate with data scientists and software engineers to integrate models into existing systems.
• Build and maintain data and model dashboards to monitor performance and health in production.

Required Skills

• 5+ years of hands-on experience in ML operations or ML engineering.
• Experience with AWS or Azure cloud platforms, specifically AWS SageMaker.
• Proficiency in REST API development and AWS Networking protocols.
• Experience with containerization using Docker.
• Knowledge of CI/CD pipelines and infrastructure components.
• Strong understanding of software engineering principles, version control, code review, and testing.
• Bachelor's degree in Computer Science, Data Science, or a related field.

Preferred Skills

• Master's or Ph.D. degree.

Key Skills

Education

Bachelor’s Degree