AI Pipeline Engineer
Tasks
- Build evaluation dataset construction pipelines with integrity controls
- Build high throughput data loading to maximize GPU utilization
- Build ingestion systems for multimodal data
- 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 for data quality drift and pipeline health
- Implement data cleaning deduplication and filtering
- Implement data privacy redaction and consent enforcement
- Implement data quality assurance
- Implement labeling workflows active learning and human in the loop
- Optimize cost and performance using compression format selection and caching
Perks/Benefits
Skills/Tech-stack
Active Learning | Apache Beam | Apache Spark | CI/CD | Data Lineage | Data Modeling | Data Privacy | Data Quality | Data loading | Data quality assurance | Dataset versioning | Distributed Systems | High Throughput | High Throughput Data Loading | High-throughput data | Human-in-the-loop | Java | Machine Learning | Machine Learning Workflows | Multimodal Data | Observability | Python | Quality Assurance | Ray | Rédaction | Scala | Storage Optimization | Testing | The Loop
Education
Roles
AI | AI Pipeline Engineer | Data Engineer | Engineer | Pipeline Engineer
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