Role Description 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. 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. Requirements - 100% Remote (Continental United States) - 6+ years of experience - Full-time, direct W2 with Bright Vision Technologies (no C2C, no 1099, no third-party) - No new H1B sponsorship available. H1B transfers welcomed for qualified candidates. - Technical coding assessment is mandatory. Benefits - Competitive base salary commensurate with experience, plus benefits. How to Apply For immediate consideration, please send your resume to [email protected] or contact us at (908) 505-3545.

• Installing, configuring, and troubleshooting Windows Server environments • Writing SQL queries and MS SQL administration • Managing virtual machines using VMWare and OpenStack • Working with Red Hat OpenShift environments • Familiarity with load balancing concepts and multi-data center environments • Utilizing IaC tools (Terraform or Ansible) for deployment automation • Automating Windows administrative tasks using PowerShell.

• Design and automate RF design workflows to optimize cost and time • Assess and develop cutting-edge wireless propagation models • Translate complex regulatory guidelines into efficient RF engineering workflows • Continuously research and improve RF propagation modeling and predictions • Provide expert guidance to E2E teams on RF engineering best practices • Identify and resolve challenges creatively, proactively future proofing infrastructure • Constantly optimize and simplify propagation modelling and geodata assets

• Operate expert-level knowledge of Linux environments, kernel tuning, and advanced administration. • Oversee advanced network routing, switching, load balancing, and firewall configurations. • Lead the strategy and implementation of Infrastructure as Code (IaC) to automate server and network device management. • Act as the primary technical authority, providing consulting during project initiation. • Continuously evaluate and optimize network throughput, Linux OS performance, and overall system availability.