01Overview
Role: Computer Vision Engineer
Experience: 2-4 Years
Location: Kondapur, Hyderabad
We are currently looking for immediate joiners for multiple open positions. Candidates who are available to join at the earliest or serving short notice periods are encouraged to apply.
About the Role:
The ideal candidate should have a deep understanding of computer vision fundamentals,
real-time AI inference, edge optimization, and embedded system constraints. This role involves
working on robotics and intelligent systems requiring low-latency perception, tracking,
navigation, and human interaction capabilities.
Key Responsibilities
Design, develop, and optimize computer vision pipelines for edge devices. Deploy deep learning models on embedded platforms such as Jetson, Raspberry Pi, RK3588, Intel NUC, and other industrial SBCs. Build real-time systems. Optimize AI inference pipelines.
Work with camera interfaces including:
CSI cameras USB cameras Stereo cameras Industrial machine vision cameras Develop low-latency pipelines using OpenCV, GStreamer, and hardware accelerators. Work closely with robotics, AI/ML, and embedded teams for end-to-end product integration. Debug performance bottlenecks involving memory, thermal limits, FPS, and power optimization on edge hardware.
Required Skills:
Core Computer Vision Knowledge
Strong understanding of:
Image processing fundamentals Camera geometry & calibration Feature extraction & matching CNN architectures for vision Transformer-based vision models Real-time perception systems
Technical Skills:
Strong proficiency in Python and/or C++ OpenCV (advanced level) Deep learning frameworks:
PyTorch TensorFlow
Experience with:
TensorRT CUDA ONNX GStreamer OpenVINO Linux/Ubuntu development environment Model optimization for edge inference
Embedded & Edge AI:
Hands-on experience with: NVIDIA Jetson Nano / Xavier / Orin RK3588 or similar ARM SBCs Industrial embedded AI hardware Hardware acceleration and low-power AI deployment
Preferred Qualifications:
Experience in robotics or autonomous systems Experience with real-time video analytics Knowledge of sensor fusion (IMU + Vision) Experience with edge AI model compression techniques Understanding of hardware-software co-optimization Experience building production-grade AI systems .
