AI Data Infrastructure Engineer
Tasks
- Build high throughput data loading for training
- Build ingestion systems for multimodal data
- Collaborate with ML researchers and engineers
- Construct evaluation datasets with integrity controls
- Design AI data pipelines
- Design storage architectures for cost throughput and latency
- Develop dataset versioning and lineage
- Document data schemas and operational procedures
- Drive observability for data quality drift and pipeline health
- Implement data privacy redaction and consent enforcement
- Implement data quality assurance and validation
- Implement labeling workflows and active learning
- Optimize data cost and performance with compression formats and caching
- Perform data cleaning deduplication and filtering
Perks/Benefits
Skills/Tech-stack
Active Learning | Apache Beam | CI/CD | Code review | Data Governance | Data Lineage | Data Modeling | Data Privacy | Data Quality | Data Storage | Data Versioning | Data redaction | Distributed Systems | GPU Utilization | Java | Labeling | Machine Learning | Python | Ray | Scala | Spark | Testing
Education
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