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Computer Vision Data Scientist (Pune)

icon building Company : Codvo.ai
icon briefcase Job Type : Full Time

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Job Description - Computer Vision Data Scientist (Pune)



Job Title: Computer Vision Data Scientist

About Us

At Codvo, we are committed to building scalable, future-ready data platforms that power business impact. We believe in a culture of innovation, collaboration, and growth, where engineers can experiment, learn, and thrive. Join us to be part of a team that solves complex data challenges with creativity and cutting-edge technology.


Key Responsibilities
  • Design, develop, and optimize computer vision models for:
  • Object Detection
  • Image & Instance Segmentation
  • Multi-Object Tracking
  • Human Pose Estimation
  • Build and train deep learning models using frameworks such as PyTorch or TensorFlow.
  • Convert and optimize models for edge deployment using:
  • TensorRT
  • OpenVINO
  • NVIDIA TAO Toolkit
  • Perform model quantization, pruning, benchmarking, and latency optimization.
  • Deploy models on edge hardware platforms (NVIDIA Jetson, Intel-based edge devices, etc.).
  • Write clean, scalable, and production-ready code in Python.
  • Collaborate with cross-functional teams including ML engineers, software developers, and hardware teams.
  • Conduct performance evaluation, debugging, and continuous improvement of deployed systems.
  • Stay updated with the latest research and advancements in computer vision and edge AI.

Required Qualifications
  • Strong expertise in:
  • Object detection architectures (e.g., YOLO, Faster R-CNN, SSD)
  • Segmentation models (e.g., U-Net, Mask R-CNN)
  • Tracking algorithms (e.g., DeepSORT, ByteTrack)
  • Pose estimation frameworks (e.g., OpenPose, HRNet)
  • Proven experience deploying optimized models using:
  • TensorRT
  • OpenVINO
  • NVIDIA TAO Toolkit
  • Strong Python programming skills.
  • Experience with model optimization techniques (INT8 quantization, FP16, pruning).
  • Familiarity with ONNX and model conversion pipelines.
  • Understanding of GPU acceleration and edge hardware constraints.








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