Description

You will own the end-to-end ML lifecycle, from data ingestion to model deployment and monitoring, ensuring stability and automation across the infrastructure.

This role is hybrid.

Responsibilities

• Design and implement MLOps pipelines to streamline the machine learning lifecycle.
• Develop and maintain CI/CD pipelines for efficient model deployment.
• Build and manage ML infrastructure on AWS, primarily using Amazon SageMaker.
• Monitor model performance in production and identify optimization opportunities.
• Collaborate with data scientists and engineers to improve development processes and data quality.

Required Skills

• 7+ years of experience in Ops Engineering.
• 4+ years of hands-on Machine Learning experience.
• Strong proficiency in Python programming.
• Experience with ML frameworks: TensorFlow, PyTorch, or Scikit-learn.
• Expertise in AWS, specifically Amazon SageMaker.
• Deep knowledge of MLOps tools: Docker, Kubernetes, Jenkins, Airflow.
• Proficiency with Git version control.
• Understanding of data engineering concepts, SQL, and ETL pipelines.

Preferred Skills

• Experience with cloud-based data storage and processing (S3, EMR, Redshift).
• Knowledge of DataOps and automation processes.

Key Skills

Education

Any Graduate