AI Data Infrastructure Engineer
Tasks
- Build high throughput data loading for GPU training
- Build ingestion systems for multimodal data
- Create evaluation dataset pipelines with integrity controls
- Design data pipelines for AI training and evaluation
- Design storage architectures for cost throughput and latency
- Develop dataset versioning and lineage tracking
- Document data systems schemas and operational procedures
- Drive observability for data quality and pipeline health
- Implement data cleaning and quality assurance
- Implement data privacy redaction and consent enforcement
- Implement labeling and active learning workflows
- Optimize cost and performance with compression formats and caching
Perks/Benefits
Skills/Tech-stack
Active Learning | Apache Beam | CI/CD | Caching | Code review | Compression | Data Deduplication | Data Lineage | Data Modeling | Data Privacy | Data Quality | Data Versioning | Data cleaning | Data provenance | Data redaction | Distributed Systems | GPU Training | Human-in-the-loop | Java | Labeling workflows | Observability | Python | Ray | Scala | Spark | Storage Systems | Testing | The Loop
Education
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