AI Infrastructure Engineer
Role details
Job location
Tech stack
Job 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 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, and developer experience for ML engineers and researchers, with strong emphasis on reliability, efficiency, and cost control. 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., * 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.
Requirements
- 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.
Benefits & conditions
This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential. AI Infrastructure Engineer Job Title: AI Infrastructure Engineer 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