AI Data Infrastructure Engineer at Bright Vision Technologies

Silver Springs, New York, United States -

Full Time

Start Date

Immediate

Expiry Date

12 Sep, 26

Salary

150000.0

Posted On

14 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Yes

Skills

Python, JVM, Spark, Ray, Beam, Distributed Systems, Data Modeling, CI/CD, Petabyte-scale Storage, Data Pipeline Design, Dataset Versioning, Multimodal Data Processing, GPU Utilization Optimization, Data Lineage, Data Quality Assurance, Software Engineering

Industry

Information Technology & Services

Description

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. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential. AI Data Infrastructure Engineer Job Title: AI Data Infrastructure Engineer Salary Range: 100k$/Annum-150k$/Annum 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. Employment Terms & Visa Policy 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. We do not engage in C2C, 1099, or third-party arrangements for this role. 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. Job Summary 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 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. How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to boon@bvteck.com or contact us at (908) 650-6699. Learn more about Bright Vision Technologies at www.bvteck.com. We recognize that our people are our strength, and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Bright Vision Technologies is an Equal Opportunity Employer, including Disability/Veterans. Position offered by “No Fee Agency.” 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.

Responsibilities

Design and operate large-scale data pipelines and ingestion systems for diverse modalities to power AI training and evaluation. Focus on data cleaning, deduplication, and high-throughput delivery to maximize GPU utilization and model quality.