About this role
Full Time Senior Computer Vision Data Scientist (Pune) in AI at Codvo.ai in Maharashtra, Pune, India. Apply directly through the link below.
At a glance
- Work mode
- Office
- Employment
- Full Time
- Location
- Maharashtra, Pune, India
- Experience
- Senior
Core stack
- Cross-functional
- Computer Vision
- Deep Learning
- Optimization
- Performance
- TensorFlow
- Innovation
- Debugging
- PyTorch
- Python
- Design
- ML
- AI
Quick answers
What skills are required?
Cross-functional, Computer Vision, Deep Learning, Optimization, Performance, TensorFlow, Innovation, Debugging, PyTorch, Python, and more.
Codvo.ai is hiring for this role. Visit career page
Pune, India
Job Title: Computer Vision Data Scientist
Key Responsibilities
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.
- 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.