Computer Vision Engineer

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