Description

You will build and maintain scalable MLOps pipelines to support the full machine learning lifecycle.

Responsibilities

• Build and maintain scalable MLOps pipelines using AWS SageMaker.
• Support the full ML lifecycle from ingestion and training to versioning and deployment.
• Optimize models for real-time inference via APIs.
• Detect and address data and model drift while automating re-training workflows.
• Architect end-to-end AI/ML solutions on Databricks.

Required Skills

• 12+ years of professional experience.
• 5+ years in MLOps, ML Engineering, or Data Engineering.
• Deep AWS SageMaker experience, including Pipelines, Studio, and Model Hosting.
• Hands-on experience managing large-scale data involving billions of records.
• Experience with real-time model deployment requiring <100ms latency.
• Proficiency in Python, Docker, and Bash.
• 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