Data Infrastructure & MLOps Engineer (all genders)
Tasks
- Build and maintain MLOps workflows
- Build data ingestion transformation storage and serving
- Build secure reproducible development testing and production environments
- Define service level objectives runbooks alerts dashboards and on call processes
- Design scalable data infrastructure
- Develop batch and streaming data pipelines
- Establish data platform standards
- Implement Infrastructure as Code and automated deployment
- Implement model validation monitoring drift detection and incident response
- Improve data discoverability and usability
- Investigate incidents and perform root cause analysis
- Manage cloud data and machine learning infrastructure
- Manage model versioning training evaluation deployment and rollback
- Operate model serving and feature pipelines
- Protect sensitive data with access controls encryption and secrets management
- Support compliance privacy and responsible AI documentation
Perks/Benefits
- N/A
Skills/Tech-stack
Access Control | Airflow | CI/CD | DBT | Data Governance | Data Lakes | Data Pipelines | Data Versioning | Data Warehousing | Docker | Encryption | Feature Engineering | Incident Management | Infrastructure as Code | Kafka | Kubernetes | MLflow | Machine Learning | Model Deployment | Model Monitoring | Model versioning | Observability | Performance optimization | Python | SQL | Secrets management | Spark | Terraform | Workflow Orchestration | “as-code”
Education
N/A
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