AI Data Infrastructure Engineer
Tasks
- Build evaluation dataset construction pipelines with integrity and contamination controls
- Build high throughput data loading systems
- Build ingestion systems for multimodal data
- Collaborate with ML researchers and engineers on data system requirements
- Design and operate large scale data pipelines for AI training and evaluation
- Design storage architectures for 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 pipelines
- Optimize cost and performance using compression format selection and caching
Perks/Benefits
Skills/Tech-stack
Active Learning | Apache Beam | Apache Spark | CI/CD | Caching | Code review | Compression | Data Lineage | Data Modeling | Data Privacy | Data Quality | Data Versioning | Data loading | Data redaction | Dataset versioning | Distributed Systems | High Throughput | High Throughput Data Loading | High-throughput data | Human-in-the-loop | JVM | Python | Ray | Storage Architecture | Testing | The Loop
Education
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