AI Data Infrastructure Engineer
Tasks
- Build evaluation dataset construction pipelines with integrity controls
- Build high throughput data loading systems for GPU utilization
- Build ingestion systems for multimodal data
- Collaborate with ML researchers to align data systems with model needs
- Design large scale AI data pipelines supporting training and evaluation
- Design storage architectures balancing cost throughput and latency
- Develop dataset versioning lineage and provenance tracking
- Document data systems schemas and operational procedures
- Drive observability of data quality drift and pipeline health
- Implement data cleaning deduplication filtering and quality assurance
- Implement data privacy redaction and consent enforcement
- Implement labeling workflows active learning and human in the loop improvement
- Optimize cost and performance using compression format selection and caching
Perks/Benefits
- N/A
Skills/Tech-stack
Apache Beam | CI/CD | Caching | Code review | Compression | Data Deduplication | Data Filtering | Data Formats | Data Ingestion | Data Lineage | Data Modeling | Data Pipelines | Data Privacy | Data Quality | Data Storage | Data provenance | Dataset versioning | Distributed Systems | GPU Utilization | High Throughput | High-throughput computing | Java | Python | Ray | Rédaction | Scala | Spark | Testing
Education
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