
MLOps/AWS Data Engineer
2 days ago
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.
- Maintain comprehensive documentation of pipelines, systems, and processes.
- Participate in Agile ceremonies, sprint planning, and technical reviews.
Required Skills & Qualifications :
years of hands-on experience in data engineering, MLOps, or cloud-native ML/AI systems.
- Proficiency in Python with experience in writing production-grade code.
- Strong experience with AWS services : SageMaker, Glue, Lambda, ECS/EKS, CloudFormation/Terraform, CloudWatch, Step Functions, S3, Redshift, Athena
- Experience with CI/CD tools : Git, GitHub/GitLab, Jenkins, AWS CodePipeline.
- Hands-on with Docker and container orchestration.
- Experience working with Apache Spark / PySpark for large-scale data processing.
- Solid understanding of machine learning lifecycle (training, validation, deployment, monitoring).
- Strong SQL skills and experience working with large datasets.
Preferred Qualifications :
- Experience with Kubeflow, MLflow, or similar MLOps frameworks.
- Familiarity with Kafka, Airflow, or Apache NiFi for orchestration.
- AWS Certifications (e.g., AWS Certified Machine Learning Specialty, AWS Data Analytics, or Solutions Architect).
- Exposure to data governance, data privacy, and compliance frameworks.
- Prior experience in Agile/Scrum environment.
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