Role Description We are seeking an AI Infrastructure Engineer to design, build, and operate the platform layer that powers large-scale AI training and inference workloads. The role focuses on: - GPU clusters - Distributed training frameworks - Scheduling - Storage performance - Developer experience for ML engineers and researchers The ideal candidate has built or operated production AI infrastructure at scale, understands the interaction between hardware, kernel, scheduler, and ML framework, and brings strong software engineering discipline to platform work. Key Responsibilities - Design and operate GPU and accelerator infrastructure for training and inference, spanning on-prem clusters, cloud-managed services, and hybrid configurations. - Build scheduling, queueing, and resource-sharing systems that maximize accelerator utilization across many teams. - Integrate frameworks such as PyTorch, JAX, DeepSpeed, FSDP, Megatron-LM, and Ray Train into a unified platform offering. - Operate high-performance storage systems and data pipelines that keep accelerators fed with training data at near-line-rate. - Design networking architectures supporting RDMA, InfiniBand, NCCL, and high-bandwidth collective communication. - Build observability for AI workloads including utilization, throughput, training stability, and failure-mode analytics. - Implement checkpointing, restart, and fault-tolerance patterns for long-running training jobs at scale. - Drive cost optimization across compute, storage, and networking through scheduling, spot capacity, and right-sizing. - Develop developer tooling and paved-road workflows that let researchers launch experiments safely and efficiently. - Partner with research and applied ML teams to plan capacity for upcoming training runs. - Implement security controls, isolation, and access management for multi-tenant AI infrastructure. - Drive automation across cluster provisioning, lifecycle management, and configuration enforcement. - Maintain runbooks, capacity dashboards, and operational documentation for the AI platform. - Stay current with AI infrastructure research, accelerator hardware, and emerging open-source AI tooling. Qualifications - Bachelor’s or Master’s degree in Computer Science or a related field. - Six or more years of experience in infrastructure, platform, or HPC engineering. - Hands-on experience operating GPU clusters or large-scale ML training infrastructure. - Strong proficiency in Python and at least one systems language such as Go or C++. - Deep understanding of distributed training, accelerator architectures, and collective communication. - Experience with Kubernetes, Slurm, Ray, or similar scheduling systems for ML workloads. - Strong understanding of Linux internals, networking, and high-performance storage. - Experience with at least one major cloud provider’s ML infrastructure offerings. - Strong software engineering practices including testing, CI/CD, and code review. - Excellent communication and cross-functional collaboration skills. Preferred Qualifications - Experience operating InfiniBand or RDMA networking at scale. - Contributions to open-source ML infrastructure projects. - Familiarity with custom orchestrators or research-grade training stacks. - Exposure to frontier model training operations. - Experience with FinOps for AI workloads. 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 .

• Coordinating requests for quotes with manufacturers • Executing quotes in conjunction with stakeholders • OSP Shortage Mitigation: Managing risks and constraints in the OSP supply chain. • Logistics Oversight: Resolving critical, real-time onsite delivery queries. • Financial Controls: Conducting regular cost variance and budget checks. • Issue Resolution: Tracking kick-off bugs and driving PCG/TEC resolutions. • EMEA/APAC Deployment: Overseeing the ongoing regional OFCI implementation. • Vendor Alignment: Leading strategic syncs with key manufacturers. • SOX control responsibilities may be part of this role, which are to be adhered to where applicable.

• Plan, design, and build - Secure infrastructure for AI-assisted development and testing - Data-access security across the platform - Cross-cutting application features You will manage your own projects, communicating designs, plans, and status updates proactively. We work in Go, Python, and TypeScript with a focus on reliability and observability.

• Design, build, and maintain scalable, secure, and cost-efficient cloud infrastructure on AWS using Infrastructure as Code best practices. • Own and evolve our Atmos-based IaC framework, including stack configuration, component architecture, and environment lifecycle management. • Develop and maintain Terraform and OpenTofu modules and components, ensuring reusability, consistency, and DRY configuration across environments. • Lead and mature our CI/CD infrastructure pipelines using GitHub Actions, enabling reliable and automated infrastructure deployments. • Manage containerized workloads using Docker and Kubernetes (Helm), supporting our engineering and data science teams. • Build and maintain observability infrastructure using Prometheus and Grafana to ensure system reliability, performance visibility, and rapid incident response. • Collaborate closely with engineers, scientists, and other stakeholders to translate requirements into robust infrastructure solutions. • Lead and participate in development lifecycle activities including design, code review, testing, and production releases. • Mentor and support other engineers, deeply reviewing infrastructure code and elevating engineering standards across the team. • Contribute to our evolving engineering culture, standards, tooling, and processes. • Comply with Vibrant Planet’s Information Security Policy and the full security responsibilities detailed in the Employee Handbook, including complete required security training, safeguard customer and company data, keep credentials secure, and report suspected security incidents or policy violations through established channels. • Follow secure development practices, adhere to established change management processes for production systems, protect the confidentiality and integrity of customer data, and promptly address security vulnerabilities in your area of responsibility.