Infrastructure Reliability Engineer

Bright Vision Technologies•Overland Park, KS

3d•$100,000 - $150,000•Remote

About The Position

Bright Vision Technologies is seeking an Infrastructure Reliability Engineer to build and operate large-scale data systems supporting AI training and evaluation pipelines. The role combines data engineering expertise with an understanding of AI workloads, covering data ingestion, transformation, quality assurance, lineage, and high-throughput delivery across diverse modalities. The position calls for experience operating petabyte-scale data systems and understanding how infrastructure choices affect model quality and training efficiency. This is a full-time, direct W2 position that is 100% remote in the United States. The salary range is $125,000–$170,000 annually. The role requires six or more years of experience.

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.

Nice To Haves

Experience with multimodal datasets at large scale.
Familiarity with data quality tooling and dataset evaluation methodology.
Exposure to privacy-preserving data systems and regulated data handling.
Open-source contributions to data infrastructure projects.
Experience supporting frontier model training pipelines.

Responsibilities

Design and operate large-scale data pipelines for AI training, evaluation, and continual improvement workflows.
Build ingestion systems for 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 to maximize GPU utilization during training.
Implement labeling workflows, active learning pipelines, and human-in-the-loop data improvement systems.
Design storage architectures that balance 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.