Infrastructure Reliability Engineer

Bright Vision Technologies; Wildwood (MO); Remote; USD 125,000 - 170,000; Full time

Job summary

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This role focuses on building and operating large-scale data systems powering AI training and evaluation pipelines, with emphasis on ingestion, transformation, quality assurance, lineage, and high-throughput delivery across modalities.

The candidate should have 6+ years in data engineering for ML/AI workloads, strong Python, and

Qualifications

• Bachelor's or Master's degree in Computer Science or related field.
• 6+ years of data engineering experience, with ML/AI workloads.
• Strong Python proficiency and at least one JVM or systems language.
• Deep experience with data processing frameworks such as Spark, Ray, or Beam.
• Hands-on experience with petabyte-scale storage and pipelines.
• Understanding of distributed systems, data modeling, and storage formats.
• Experience with dataset versioning, lineage, and reproducibility for ML workflows.
• Experience 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.

Responsibilities

• Design and operate large-scale data pipelines for AI training and evaluation.
• Build ingestion systems for multiple modalities including text, image, audio, video, and structured signals.
• Implement data cleaning, deduplication, filtering, and QA at petabyte scale.
• Develop dataset versioning, lineage, and provenance tracking for reproducible training.
• Build high-throughput data loading systems to maximize GPU utilization.
• Implement labeling workflows and active learning pipelines.
• Design storage architectures balancing cost, throughput, and latency.
• Create pipelines for evaluation dataset construction with integrity controls.
• Ensure data privacy, redaction, and consent enforcement across pipelines.
• Collaborate with ML researchers and engineers to align data systems with model needs.
• Drive observability of data quality, drift, and pipeline health.
• Optimize cost and performance via compression, format selection, and caching.
• Document data systems and procedures for internal use.
• Stay current with AI data infrastructure research and open-source tools.

Skills

Python

JVM

Systems programming

Spark

Ray

Beam

Data engineering

CI/CD

Communication

Education

Bachelor's or Master's degree in Computer Science

Tools

Spark

Ray

Beam

CI/CD tooling

Job description

Infrastructure Reliability Engineer – Remote

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.

Job Title:Infrastructure Reliability Engineer

Location:100% Remote (U.S.)

Position Type:Full-time, Direct W2

Salary Range:$125,000–$170,000 Annually

Experience Required:6+ years

Job Summary

We are seeking an Infrastructure Reliability Engineer to build and operate the large-scale data systems that power modern AI training and evaluation pipelines. The role combines deep data engineering expertise with a strong understanding of AI workloads, focusing on ingestion, transformation, quality assurance, lineage, and high-throughput delivery of data to training jobs across diverse modalities. The ideal candidate has experience operating petabyte-scale data systems, strong software engineering fundamentals, and clear understanding of how data infrastructure choices propagate into model quality and training efficiency.

Key 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 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.

Required Qualifications

• 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.

Preferred Qualifications

• 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.

Bright Vision Technologies is an Equal Opportunity Employer.

Equal Employment Opportunity (EEO) Statement

Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall.

BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.