Description

You will manage the infrastructure and deployment lifecycle of machine learning models from research to production.

Responsibilities

• Build scalable infrastructure to support ML model production integration.
• Develop and maintain CI/CD pipelines specifically for ML model deployment.
• Deploy models using TensorFlow Serving, PyTorch Serving, Triton Inference Server, or TensorRT.
• Monitor model performance in production environments.
• Collaborate with data scientists and software engineers to transition models into production.

Required Skills

• 4-6 years of experience in ML project management with a recent focus on MLOps.
• Proficiency in Docker and Kubernetes for containerization and orchestration.
• Hands-on experience with AWS, Azure, or GCP.
• Experience developing and maintaining CI/CD pipelines.
• Knowledge of AI technologies deployment, data engineering, and scalability testing.
• Understanding of security practices and IT performance.
• Implement security protocols and ensure regulatory compliance within ML systems.

Preferred Skills

• Bachelor’s or Master’s degree in Computer Science or a related field.

Key Skills

Education

Bachelor’s/Master’s degree in Computer Science