Description

You will manage the deployment, scaling, and lifecycle of machine learning models in production environments.

Responsibilities

• Design and implement scalable pipelines to deploy machine learning models, including LLMs, in production.
• Build and manage CI/CD pipelines to ensure continuous delivery of ML models.
• Develop automation scripts to streamline data preprocessing, model training, and evaluation workflows.
• Design monitoring solutions to ensure observability and reliability of deployed models.
• Optimize model performance and infrastructure for scalability and efficiency.

Required Skills

• 6+ years of professional experience, with at least 4+ years specifically in an MLOps role.
• Expertise in Python for deploying both classical and deep learning models.
• Hands-on experience with MLOps frameworks such as SageMaker or Databricks.
• Proficiency with DevOps tools including Docker, Kubernetes, and Terraform.
• Strong command of version control systems like Git.
• Experience deploying deep learning models in production environments at scale.
• Ability to manage deployments across cloud platforms including AWS, Azure, or Google Cloud.
• Knowledge of SQL, NoSQL, and ETL processes for data pipelines.
• Experience validating deployments through performance testing frameworks to identify bottlenecks.

Preferred Skills

• Experience deploying GenAI solutions in production environments.
• Familiarity with model hubs and no-code integration platforms like Hugging Face and AWS Bedrock.

Key Skills

Education

Any Gradute