Senior AI Data Infrastructure Engineer – Remote

Bright Vision Technologies; Euless, Bedford (TX, TX); On-site; USD 125,000 - 170,000; Full time

Job summary

Bright Vision Technologies is seeking an Infrastructure Reliability Engineer to build and operate large-scale data systems powering AI training and evaluation. This remote U.S.

role emphasizes ingestion, transformation, quality assurance, and lineage across petabyte-scale datasets. The successful candidate will have 6+ years of data engineering experience, strong Python, and experience with Spark, Ray, or Beam.

Qualifications

• Bachelor's or Master's degree in Computer Science or a related field.
• 6+ 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.

Responsibilities

• 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 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.

Skills

Python

Distributed systems

Data engineering

Cross‑functional collaboration

Education

Bachelor's or Master's in Computer Science or related field

Tools

Spark

Ray

Beam

CI/CD tooling

Job description

Bright Vision Technologies is seeking an Infrastructure Reliability Engineer to build and operate large-scale data systems powering AI training and evaluation. This remote U.S.

role emphasizes ingestion, transformation, quality assurance, and lineage across petabyte-scale datasets. The successful candidate will have 6+ years of data engineering experience, strong Python, and experience with Spark, Ray, or Beam.