Description

You will architect and maintain scalable machine learning pipelines and end-to-end AI/ML solutions.

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 with sub-100ms latency.
• Detect and address data or model drift and automate re-training workflows.
• Architect end-to-end solutions on Databricks using feature stores and model registries.

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.
• Proven experience with real-time model deployment and low-latency inference.
• Proficiency in Python.
• Experience with Docker, Bash, and Databricks.
• Knowledge of CI/CD for ML and model monitoring.

Preferred Skills

• Experience with Terraform or CloudFormation.

Key Skills

Education

Any Graduate