AI Data Infrastructure Engineer

Location

Kentucky

Posted

11 days ago

Salary

0

Seniority

Senior

Bachelor Degree6 yrs expEnglishDistributed SystemsPythonRaySpark

Job Description

AI Data Infrastructure Engineer

Bright Vision Technologies

• Design and operate large-scale data pipelines supporting AI training, evaluation, and continual improvement workflows. • Build ingestion systems for diverse modalities including text, image, audio, video, and structured signals. • Implement data cleaning, deduplication, filtering, and quality assurance at petabyte scale. • Develop dataset versioning, lineage, and provenance tracking systems suitable for reproducible training. • Build high-throughput data loading systems that maximize GPU utilization during training. • Implement labeling workflows, active learning pipelines, and human-in-the-loop data improvement systems. • Design storage architectures balancing cost, throughput, and latency across data tiers. • Build evaluation dataset construction pipelines with strict integrity and contamination controls. • Implement data privacy, redaction, and consent enforcement throughout the pipeline. • Collaborate with ML researchers and engineers to align data systems with model development needs. • Drive observability of data quality, drift, and pipeline health across the AI data estate. • Optimize cost and performance through compression, format selection, and caching strategies. • Document data systems, schemas, and operational procedures for broad internal use. • Stay current with AI data infrastructure research and emerging open-source tools.

Job Requirements

  • Bachelor’s or Master’s degree in Computer Science or a related field.
  • Six or more years of data engineering experience, with significant work supporting ML or AI workloads.
  • Strong proficiency in Python and at least one JVM or systems language.
  • Deep experience with modern data processing frameworks such as Spark, Ray, or Beam.
  • Hands-on experience operating petabyte-scale storage and pipeline systems.
  • Strong understanding of distributed systems, data modeling, and storage formats.
  • Experience with dataset versioning, lineage, and reproducibility for ML workflows.
  • Familiarity with high-throughput data loading for accelerator-based training.
  • Strong software engineering practices including testing, CI/CD, and code review.
  • Excellent communication and cross-functional collaboration skills.

Benefits

  • Competitive base salary commensurate with experience, plus benefits.

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