Description

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

This role is hybrid.

Responsibilities

• Build and maintain scalable MLOps pipelines using AWS SageMaker to support the full ML lifecycle.
• Architect end-to-end solutions on Databricks, leveraging feature stores and model registries.
• Optimize models for real-time inference via APIs, ensuring sub-100ms latency.
• Detect and address data or model drift, automating re-training workflows.
• Manage large-scale data ingestion involving billions of records.

Required Skills

• 12+ years of professional experience in software or data engineering.
• 5+ years of hands-on experience in MLOps, ML Engineering, or Data Engineering.
• Deep expertise with AWS SageMaker, including Pipelines, Studio, and Model Hosting.
• Proficiency in Python for building and maintaining production-grade code.
• Experience with containerization (Docker) and scripting (Bash).
• Hands-on experience with Databricks for data processing and model management.
• Knowledge of CI/CD practices specifically for ML pipelines and model monitoring.

Preferred Skills

• Experience with infrastructure as code tools such as Terraform or CloudFormation.

Key Skills

Education

Any Graduate