AI Data Infrastructure Engineer
Tasks
- Build high throughput data loading systems
- Build ingestion systems for multimodal data
- Collaborate with ML researchers and engineers on data needs
- Construct evaluation datasets with integrity and contamination controls
- 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 filtering quality assurance
- Implement data privacy redaction and consent enforcement
- Implement labeling workflows active learning and human in the loop improvement
- Optimize cost and performance with compression format selection and caching
Perks/Benefits
Skills/Tech-stack
Active Learning | Apache Beam | Apache Spark | CI/CD | Caching | Code review | Data Compression | Data Lineage | Data Modeling | Data Privacy | Data Quality | Data Storage | Data Versioning | Data provenance | Distributed Systems | GPU Utilization | Human-in-the-loop | JVM | Machine Learning | Python | Ray | Rédaction | Testing | The Loop
Education
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