AI Infrastructure Engineer at Bright Vision Technologies

Silver Springs, New York, United States -

Full Time

Start Date

Immediate

Expiry Date

12 Sep, 26

Salary

150000.0

Posted On

14 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

GPU Cluster Management, Distributed Training, Python, Go, C++, Kubernetes, Slurm, Ray, PyTorch, JAX, DeepSpeed, Linux Internals, RDMA, InfiniBand, CI/CD, High-Performance Storage

Industry

Information Technology & Services

Description

Bright Vision Technologies is a forward-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. We leverage cutting-edge technologies to create scalable, secure, and user-friendly applications. As we continue to grow, we’re looking for a skilled AI Infrastructure Engineer to join our dynamic team and contribute to our mission of transforming business processes through technology. 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 Salary Range: 100k$/Annum-150k$/Annum 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 This is a 100% remote, full-time, direct W2 position with Bright Vision Technologies. 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. 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. Required 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 boon@bvteck.com or contact us at (908) 650-6699. Learn more about Bright Vision Technologies at www.bvteck.com. We recognize that our people are our strength, and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Bright Vision Technologies is an Equal Opportunity Employer, including Disability/Veterans. Position offered by “No Fee Agency.” Equal Employment Opportunity (EEO) Statement Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall. BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.

Responsibilities

Design and operate the platform layer for large-scale AI training and inference workloads, focusing on GPU clusters and distributed frameworks. Implement resource scheduling, high-performance networking, and observability tools to maximize accelerator utilization and efficiency.