Remote AI Data Operations Engineer - Scale ML Pipelines

Bright Vision Technologies; Euless, Bedford (TX, TX); On-site; USD 150,000 - 165,000; Full time

Job summary

Bright Vision Technologies is seeking an AI Operations Engineer to build and operate large-scale data systems powering AI training pipelines. You will own ingestion, transformation, quality assurance, lineage, and high-throughput data delivery for models across diverse modalities.

The ideal candidate has 6+ years of data engineering experience with ML/AI workloads, strong Python skills, and experience with Spark/Ray/Beam in petabyte-scale environments.

Qualifications

• Six+ years of data engineering experience with ML/AI workloads.
• Proficiency in Python and at least one JVM language.
• Experience with Spark, Ray or Beam for large-scale data processing.
• Hands-on experience with petabyte-scale storage and data pipelines.
• Strong understanding of distributed systems, data modeling, and storage formats.
• Experience with dataset versioning, lineage, and reproducibility in ML workflows.
• Knowledge of high-throughput data loading for AI training.
• Solid software engineering practices: testing, CI/CD, code reviews.
• Excellent communication and cross-functional collaboration skills.

Responsibilities

• Build and operate data systems powering AI training and evaluation pipelines.
• Ingest, transform, validate, and ensure data quality for models.
• Manage data lineage and reproducibility across ML workflows.
• Ensure scalable and efficient data delivery to training jobs across modalities.
• Collaborate with data scientists, software engineers, and ops teams.

Skills

Python

Java

Systems language

Spark

Ray

Beam

Big data storage

Distributed systems

Data modeling

Data lineage

Data quality

CI/CD

Testing

Communication

Cross-functional teamwork

Tools

CI/CD tools

Job description

Bright Vision Technologies is seeking an AI Operations Engineer to build and operate large-scale data systems powering AI training pipelines. You will own ingestion, transformation, quality assurance, lineage, and high-throughput data delivery for models across diverse modalities.

The ideal candidate has 6+ years of data engineering experience with ML/AI workloads, strong Python skills, and experience with Spark/Ray/Beam in petabyte-scale environments.