Bright Vision Technologies is seeking a highly experienced AI Systems Performance Specialist with 10+ years of experience in AI infrastructure, machine learning systems, High-Performance Computing (HPC), and performance engineering. The ideal candidate will optimize AI training and inference workloads for maximum performance, scalability, reliability, and cost efficiency. This role requires deep expertise in GPU optimization, distributed training, Large Language Model (LLM) inference, Python, C++, CUDA, and production AI systems , along with the ability to lead performance optimization initiatives across enterprise-scale AI platforms.
Key Skills for This Role
PythonC++CUDANCCLDeepSpeedPyTorch Distributed
Full Job Posting
Location
100% Remote (Continental United States) Position Type: Full-time, Direct W2 Salary Range: $130,000–$180,000 Annually Experience Required: 10+ Years
Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position.
Job Summary
Bright Vision Technologies is seeking a highly experienced AI Systems Performance Specialist with 10+ years of experience in AI infrastructure, machine learning systems, High-Performance Computing (HPC), and performance engineering. The ideal candidate will optimize AI training and inference workloads for maximum performance, scalability, reliability, and cost efficiency. This role requires deep expertise in GPU optimization, distributed training, Large Language Model (LLM) inference, Python, C++, CUDA, and production AI systems , along with the ability to lead performance optimization initiatives across enterprise-scale AI platforms.
Key Responsibilities
Optimize AI training and inference pipelines for maximum throughput, low latency, scalability, and infrastructure efficiency.
Analyze and improve GPU utilization, memory management, kernel execution, and multi-GPU performance across production AI workloads.
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Design and implement optimization techniques including quantization, pruning, mixed precision, batching, caching, speculative decoding, and model parallelism .
Profile AI applications using industry-standard performance analysis tools and identify bottlenecks across compute, memory, networking, and storage.
Optimize distributed training and inference using NCCL, DeepSpeed, PyTorch Distributed, Ray, MPI, or similar distributed computing frameworks .
Collaborate with AI researchers, ML engineers, platform engineers, and infrastructure teams to improve model performance and production reliability.
Evaluate emerging AI hardware, GPU architectures, inference frameworks, and optimization technologies to improve enterprise AI capabilities.
Drive AI infrastructure cost optimization through efficient resource utilization, cloud optimization, and FinOps best practices.
Mentor engineering teams and provide technical leadership on AI systems architecture, GPU optimization, and performance engineering.
Required Qualifications
Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, Artificial Intelligence, or a related technical discipline.
10+ years of professional experience in performance engineering, AI infrastructure, machine learning systems, High-Performance Computing (HPC), or distributed computing.
Expert-level programming skills in Python and C++ .
Extensive experience optimizing GPU-accelerated AI workloads using CUDA , distributed training frameworks, and modern deep learning libraries.
Strong knowledge of Large Language Models (LLMs), deep learning frameworks, model serving, and production AI inference.
Hands-on experience with profiling tools such as NVIDIA Nsight Systems, Nsight Compute, PyTorch Profiler, TensorBoard, or similar performance analysis tools .
Experience deploying and optimizing AI workloads on AWS, Microsoft Azure, or Google Cloud Platform (GCP) .
Strong understanding of distributed systems, networking, storage optimization, and AI infrastructure architecture.
Excellent analytical, troubleshooting, communication, and technical leadership skills.
Preferred Qualifications
Experience optimizing production-scale LLM inference and serving large foundation models.
Hands-on experience with vLLM , TensorRT-LLM , DeepSpeed , Triton Inference Server , CUTLASS , FasterTransformer , or similar AI optimization frameworks.
Knowledge of model compression, KV cache optimization, speculative decoding, and advanced inference optimization techniques.
Experience implementing FinOps strategies for AI infrastructure cost optimization and resource management.
Contributions to AI systems research, open-source AI infrastructure projects, patents, or technical publications.
Familiarity with emerging AI accelerator technologies, including AMD ROCm , Intel oneAPI , or custom AI hardware.
Interested in this opportunity? Apply today for immediate consideration! Email your updated resume: jaya@bvteck.com Call or Text: (908) 505-3545 Learn more: www.bvteck.com
Bright Vision Technologies is an Equal Opportunity Employer.
About Bright Vision Technologies
Software & SaaS51 employeesFounded 2020
AI-powered enterprise automation and software development firm.
