Description

You will architect end-to-end MLOps solutions to streamline machine learning workflows from experimentation through production deployment.

Responsibilities

• Design and implement MLOps platforms using Kubeflow, Metaflow, Ray, MLflow, or Azure ML.
• Design automated CI/CD pipelines for machine learning models transitioning to production.
• Implement monitoring, logging, and governance practices for deployed ML models.
• Ensure scalability and performance of MLOps solutions across cloud and on-premise environments.
• Oversee the full lifecycle of ML models, including training, serving, and retraining.

Required Skills

• 8+ years experience in data science, machine learning, and MLOps with platform implementation.
• Proficiency in Python and Bash scripting.
• Expertise with cloud platforms including AWS, Azure, and GCP.
• Hands-on experience with Kubeflow, Metaflow, Ray, MLflow, or Azure ML.
• Experience with Docker and Kubernetes for containerized deployments.
• Knowledge of workflow orchestration tools such as Apache Airflow or Prefect.
• Ability to evaluate and benchmark ML tools based on scalability and requirements.

Key Skills

Education

Any Graduate