Description

You will manage the full lifecycle of machine learning models from development through deployment and maintenance.

Responsibilities

• Develop modular ML models in Python using object-oriented programming.
• Build, deploy, and manage ML pipelines using AWS SageMaker.
• Implement scalable solutions using Docker and serverless architectures like AWS Lambda.
• Design event-driven architectures and triggers to automate ML workflows.
• Manage model versioning and lifecycle using DVC and Git.
• Maintain CI/CD pipelines and feature stores to optimize model reuse and deployment.
• Evaluate and fine-tune model performance using statistical metrics.

Required Skills

• 5+ years of experience in machine learning and data science models.
• Proficiency in Python and object-oriented programming.
• Hands-on experience with AWS SageMaker and AWS Lambda.
• Experience with Docker and containerized environments.
• Strong understanding of statistics and data science modeling.
• Practical knowledge of Git and DVC for version control.
• Ability to design event-driven and serverless architectures.
• Degree in any field (Any Graduate).

Preferred Skills

• Experience with Large Language Models (LLMs) and advanced deep learning.
• Familiarity with Monorepos.
• Knowledge of Azure or Google Cloud platforms.

Key Skills

Education

Any Graduate