AI Data Infrastructure Engineer
Tasks
- Build data ingestion systems for multimodal data
- Build evaluation dataset pipelines with integrity and contamination controls
- Build high throughput data loading for GPU utilization
- Design and operate large scale AI data pipelines
- 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 and quality assurance
- Implement data privacy redaction and consent enforcement
- Implement labeling workflows active learning and human in the loop
- Optimize cost and performance with compression formats and caching
Perks/Benefits
Skills/Tech-stack
Apache Beam | Apache Spark | CI/CD | Caching | Code review | Compression formats | Data Governance | Data Lineage | Data Modeling | Data Privacy | Data Quality | Data loading | Data provenance | Data redaction | Dataset versioning | Distributed Systems | GPU Utilization | High Throughput | High Throughput Data Loading | High-throughput data | JVM | Java | Python | Ray | Reproducibility | Scala | Testing
Education
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