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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Data Engineer - **Company:** Bright Vision Technologies - **Location:** Hicksville, NY, United States (Remote available) - **Experience:** Expert - **Salary:** $100,000.0 - $150,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Big Data, Code Review, Continuous Integration, Data Cleansing, Data Deduplication, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Systems, Distributed Systems, Document-Oriented Databases, Java Virtual Machine (JVM), Python (Programming Language), Open Source Technology, Software Engineering, Management of Software Versions, Data Processing, Apache Spark, Caching, Storage Technologies, Information Technology, Integration Frameworks, Free and Open-Source Software, Machine Learning Operations, Data Pipelines - **Published:** July 4, 2026 - **Apply:** https://www.careerjet.com/jobad/usfe9e907ea9d4d783256d3c325aeecd93 ## About the Role * 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., * 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. ## Description 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., * 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. ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [HTTP headers that make your website go faster](https://www.wearedevelopers.com/videos/1676-http-headers-that-make-your-website-go-faster) - [Are Code Reviews Worth It? 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