AI Data Infrastructure Engineer
Tasks
- Build evaluation dataset construction with integrity controls
- Build high throughput data loading systems for GPU utilization
- Build ingestion systems for multimodal data
- Collaborate with ML researchers and engineers on data needs
- Design and operate large scale AI data pipelines
- Design storage architectures for cost throughput and latency
- Develop dataset versioning lineage and provenance tracking
- Document data systems schemas and procedures
- Drive observability for data quality drift and pipeline health
- Implement data cleaning deduplication and quality assurance
- Implement data privacy redaction and consent enforcement
- Implement labeling workflows and active learning
- Optimize cost and performance with compression format selection and caching
Perks/Benefits
- 100 percent remote
- Benefits package
- Direct W2 employment
- H1B transfer support
- Long term multi year engagement
Skills/Tech-stack
Apache Beam | CI/CD | Caching | Code review | Compression | Data Lineage | Data Modeling | Data Privacy | Data Quality | Data loading | Data redaction | Dataset versioning | Distributed Systems | GPU Utilization | High Throughput | High Throughput Data Loading | High-throughput data | Java | Provenance tracking | Python | Ray | Scala | Spark | Storage Architecture | Testing
Education
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