Data Scientist / Computer Vision Engineer

Glint Tech Solutions LLCSunnyvale, CA

About The Position

We are seeking a highly experienced Data Scientist / Computer Vision Engineer to join an innovative AI team building large-scale computer vision solutions for enterprise applications. The ideal candidate will have deep expertise in NVIDIA DeepStream, GPU optimization, distributed computing, and production-grade computer vision systems. This is a highly technical role requiring hands-on experience developing scalable, high-performance AI applications.

Requirements

  • Strong experience with Computer Vision technologies and production deployments
  • Hands-on experience with NVIDIA DeepStream (Required)
  • Strong understanding of GPU optimization and performance tuning
  • Experience building microservices architecture
  • Experience with distributed computing systems
  • Strong programming skills in Python and/or C++
  • Experience deploying scalable AI applications in production
  • Excellent problem-solving and performance optimization skills
  • NVIDIA DeepStream
  • GPU Optimization
  • Computer Vision
  • Microservices Architecture
  • Distributed Computing

Nice To Haves

  • Experience with NVIDIA CUDA or TensorRT
  • Experience with OpenCV, GStreamer, or NVIDIA SDKs
  • Familiarity with Kubernetes and Docker
  • Experience with cloud platforms (AWS, Azure, or GCP)
  • Experience deploying machine learning or deep learning models in production
  • Knowledge of real-time streaming and edge AI solutions

Responsibilities

  • Design, develop, and optimize computer vision solutions for real-world production environments
  • Build scalable AI inference pipelines using NVIDIA DeepStream
  • Optimize GPU performance for high-throughput, low-latency computer vision workloads
  • Develop and deploy distributed computer vision applications across large-scale infrastructure
  • Design and implement microservices supporting AI and computer vision platforms
  • Collaborate with AI researchers, software engineers, and infrastructure teams to deliver production-ready solutions
  • Improve system performance, scalability, and reliability across GPU-accelerated environments
  • Troubleshoot performance bottlenecks and optimize inference pipelines
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