Infrastructure Engineer

• Design, implement, and maintain distributed ingestion pipelines for structured and unstructured data (images, 3D/2D assets, binaries).
• Build scalable ETL/ELT workflows to transform, validate, and enrich datasets for AI/ML model training and analytics.
• Architect pipelines across cloud object storage (S3, GCS, Azure Blob), data lakes, and metadata catalogs.
• Optimize large-scale processing with distributed frameworks (Spark, Dask, Ray, Flink, or equivalents).
• Implement partitioning, sharding, caching strategies, and observability (monitoring, logging, alerting) for reliable pipelines.
• Support preprocessing of unstructured assets (e.g., images, 3D/2D models, video) for training pipelines, including format conversion, normalization, augmentation, and metadata extraction.
• Collaborate with ML researchers to quickly adapt pipelines to evolving pretraining and evaluation needs.

Data Infrastructure Engineer

IT Infrastructure Developer

Senior Infrastructure Engineer

Voice/Telephony Infrastructure Specialist