AI Data Infrastructure Engineer

Bright Vision Technologies is a forward-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. We leverage cutting-edge technologies to create scalable, secure, and user-friendly applications.

As we continue to grow, we’re looking for a skilled AI Data Infrastructure Engineer to join our dynamic team and contribute to our mission of transforming business processes through technology.

Job Title: AI Data Infrastructure Engineer

Location: 100% Remote (Continental United States)

Position Type: In-house Bright Vision Technologies SOW engagement (no third-party client or vendor)

Experience: 6+ years

Sponsorship: No new H1B sponsorship available. H1B transfers welcomed for qualified candidates.

Employment Type: Full-time, direct W2 with Bright Vision Technologies (no C2C, no 1099, no third-party)

Engagement: Long-term, multi-year, aligned to the Bright Vision SOW delivery roadmap

Compensation: Competitive base salary commensurate with experience, plus benefits.

This is a 100% remote, full-time, direct W2 position with Bright Vision Technologies.

This role is part of Bright Vision Technologies’ in-house Statement of Work (SOW) engagement. The client, end customer, and employer for this position is Bright Vision Technologies — there is no third-party client, vendor, or implementation partner involved.

BUT STRICTLY NO C2C/1099/3RD PARTY COMPANIES. ALL OUR ROLES ARE W2 AND NO 3RD PARTY BROKERING PLEASE.

Candidates must be willing to work directly as a full-time W2 employee of Bright Vision Technologies and contribute to our in-house SOW deliverables.

No new H1B sponsorship is available for this role.

However, candidates who are currently on a valid H1B visa and require a transfer are welcome to apply. We will support H1B transfers for qualified candidates.

For every role, a technical coding assessment is mandatory. Please apply only if you are confident in your technical abilities and hands‑on experience.

We are seeking an AI Data Infrastructure 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 a clear understanding of how data infrastructure choices propagate into model quality and training efficiency.

• 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.

• Bachelor’s or Master’s degree in Computer Science or a related field.
• Six or more years of data engineering experience, with…