• Provide services and support as a Midstream Infrastructure Engineer [Independent Engineering (IE) Advisory Services] aligned with the Technical Due Diligence & Engineering Validation For Downstream Oil & Gas / Midstream / Pipelines Functional Area / Swim Lane / Category Discipline in the Energy Industry (Oil, And Gas/Power, And Utilities) Industry Sector focussing on RM | Risk Management Solutions for clients such as U.S. Department of Energy (DOE) | DOE Energy Dominance Financing (EDF) Program. • Provides Independent Engineering advisory support for Energy Dominance Financing (EDF) Program technical due diligence, credit evaluation, lifecycle monitoring, and assurance activities, with emphasis on midstream gathering, processing, compression, storage, and transportation systems. • Reviews project documentation, evaluates technical and commercial interfaces, identifies risks and mitigations, validates assumptions, supports conditions precedent and disbursement readiness reviews where applicable, and prepares defensible work products including facility reviews, flow assurance, capacity checks, equipment evaluations, and operational readiness reports. • Coordinates with engineering, finance, legal, construction, operations, environmental, HSE, and project controls stakeholders to support timely lender and DOE decision-making.

• Build and maintain data pipelines that generate training datasets for machine learning models and experimentation • Contribute to infrastructure that supports distributed training workflows (e.g., PyTorch, Ray) • Work with workflow orchestration tools (e.g., Airflow, Flyte, or similar) to support multi-stage ML pipelines • Improve reproducibility and reliability through dataset validation, monitoring, and testing • Partner with ML engineers to support experimentation and model iteration • Help optimize performance and efficiency across data processing and training systems • Contribute to the evolution of our offline ML platform architecture as it scales

• Design, build, and evolve the cloud-native platform that powers offensive security operations • Enable engineering teams to move faster, deploy safely, and operate reliably • Create scalable self-service infrastructure and resilient platform capabilities • Help implement platform standards, security controls, and engineering best practices • Collaborate closely with software engineers to improve deployment workflows, developer experience, and platform adoption • Manage and improve the Kubernetes environment, ensuring reliability, performance, and security

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. How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to [email protected] or contact us at (908) 505-3545. Learn more about Bright Vision Technologies at www.bvteck.com .