MLOps / AWS Data Engineer

Key Responsibilities :

• Design, build, and maintain scalable MLOps pipelines using AWS tools such as SageMaker, Lambda, Step Functions, and CodePipeline.
• Automate ML workflows including data preprocessing, model training, validation, deployment, and monitoring.
• Integrate CI / CD pipelines for ML models using AWS CodeBuild, CodeDeploy, or GitHub Actions.
• Containerize ML applications using Docker and deploy using ECS / EKS.
• Implement model performance tracking, drift detection, and automatic retraining triggers.
• Develop and manage ETL / ELT pipelines using AWS Glue, AWS Lambda, and Apache Spark (PySpark).
• Build robust and scalable data ingestion workflows from structured / unstructured sources (RDS, S3, APIs, etc.).
• Manage and optimize data lakes and data warehouses using Amazon Redshift, Athena, and Lake Formation.
• Implement data validation, quality checks, and lineage tracking.
• Use Terraform or CloudFormation to automate infrastructure provisioning.
• Implement logging, monitoring, and alerting for ML systems using CloudWatch, Prometheus, or ELK Stack.
• Ensure cloud cost optimization and security best practices across environments.
• Collaborate with Data Scientists, ML Engineers, and DevOps teams to understand requirements and implement efficient

solutions.

Required Skills & Qualifications :

Preferred Qualifications :

(ref : hirist.tech)