ML Ops/Data Engineer

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Requirements:

• Experience building and maintaining ETL/ELT pipelines for large-scale data ingestion and transformation.
• Strong knowledge of AWS services for ML infrastructure, model deployment, and automation.
• Experience setting up CI/CD workflows for ML models, including versioning, monitoring, and automated retraining.
• Comfortable writing efficient Python and SQL scripts for data processing and model deployment.
• Can balance quick PoC enablement with long-term scalability in AI deployments.

Responsibilities:

• Design and maintain ETL/ELT pipelines to ingest, clean, and transform data from multiple product lines.
• Stand up and manage AWS-based ML infrastructure (e.g., S3 data lakes, AWS Glue, EMR, AWS Batch, Lambda, SageMaker).
• Own CI/CD for ML models, including environment setup, model versioning, containerization, and monitoring.
• Ensure AI teams have reliable access to data, scalable training environments, and efficient deployment pipelines.
• Help move AI proofs-of-concept from experimentation to fully productionized, scalable deployments.