Description

Build and maintain scalable MLOps pipelines to support the full machine learning lifecycle, from ingestion and training to deployment.

This role is on-site.

Responsibilities

• Architect and manage end-to-end AI/ML solutions on Databricks.
• Build and maintain MLOps pipelines using AWS SageMaker, Studio, and Model Hosting.
• Optimize models for real-time inference via APIs.
• Automate re-training workflows and address data and model drift.

Required Skills

• 12+ years of professional experience, including 5+ years in MLOps, ML Engineering, or Data Engineering.
• Deep expertise in AWS SageMaker Pipelines and real-time model deployment.
• Proficiency in Python, Docker, and Bash.
• Experience managing large-scale data involving billions of records.
• Hands-on experience with Databricks and CI/CD for ML.
• Knowledge of monitoring, feature stores, and model registries.

Preferred Skills

• Experience with Terraform or CloudFormation.

Key Skills

Education

Any Graduate