AI Infrastructure Engineer
Tasks
- Build evaluation dataset construction pipelines with integrity controls
- Build high throughput data loading for GPU utilization
- Build ingestion systems for multimodal data
- Design AI data pipelines for 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 for data quality drift and pipeline health
- Implement data cleaning deduplication filtering and quality assurance
- Implement data privacy redaction and consent enforcement
- Implement labeling workflows and active learning pipelines
- Optimize cost performance with compression formats and caching
Perks/Benefits
Skills/Tech-stack
Apache Beam | Apache Spark | CI/CD | Code review | Data Lineage | Data Modeling | Data Privacy | Data Storage | Data loading | Data redaction | Dataset versioning | Distributed Systems | GPU Utilization | High Throughput | High Throughput Data Loading | High-throughput data | JVM | Observability | Python | Ray | Reproducible ML | Testing
Education
Roles
AI | AI Infrastructure Engineer | Data Engineer | Engineer | Infrastructure Engineer
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