Description

You will build and maintain secure, automated deployment pipelines for batch inference models while driving MLOps adoption across the data science organization.

This role is on-site.

Responsibilities

• Develop and implement secure, automated deployment pipelines for existing models.
• Architect foundational deployment frameworks and introduce capabilities like MLFlow, A/B testing, and real-time endpoints.
• Educate and mentor data scientists and enablement members on MLOps practices and change management.
• Automate workflows in coordination with Model Risk Management (MRM).
• Integrate machine learning models with data management systems.

Required Skills

• 8+ years of professional experience in machine learning and MLOps.
• Proven experience building end-to-end ML models using AWS SageMaker.
• Proficiency in Python, R, SQL, and/or Java.
• Hands-on experience with CI/CD pipelines and automation tools.
• Experience with DevOps tools including Terraform, GitHub, and Jira.
• Knowledge of containerization and orchestration tools such as Docker and Kubernetes.
• Experience with data integration using IBM DB2 and Snowflake.
• Any Graduate degree.

Preferred Skills

• Experience working within highly regulated industries such as banking, finance, or healthcare.

Key Skills

Education

Any Graduate