Vision and AI Software Engineer

1 week ago

Guntur, Andhra Pradesh, India beBeesoftware Full time ₹ 10,00,000 - ₹ 15,00,000
Software Engineer - Vision and AI

About the Role:

We are seeking a talented Software Engineer to join our team and contribute to the development of innovative vision and AI solutions. As a key member of our team, you will be responsible for designing, implementing, and optimizing software modules for video capture, preprocessing, inference, and post-processing on Linux.

You will work closely with our data and ML teams to integrate computer-vision and machine-learning models on device, utilizing libraries such as OpenCV and ONNX Runtime. Your expertise in modern C++ on Linux, strong Git, and CMake skills will enable you to hit real-time targets (FPS/latency/memory) on CPU/GPU/NPU using SIMD/NEON, multithreading, and zero-copy buffers.

Key Responsibilities:

  • Own software modules for on-device video capture, preprocessing, inference, and post-processing on Linux.
  • Implement classical image processing pipelines (denoise, resize, color space, undistortion) and CV algorithms (keypoints, homography, optical flow, tracking).
  • Build and optimize distance estimation from monocular/stereo camera(s) using calibration, geometry, and/or depth-estimation networks.
  • Integrate machine learning models (PyTorch/TensorFlow → ONNX/TensorRT/NPU runtimes) for DMS/ADAS events: drowsiness, distraction/gaze, phone-usage, smoking, seat belt, etc.
  • Collaborate with data/ML teams on dataset curation, labeling specs, training/evaluation, and model handoff.

Required Skills and Qualifications:

  • 2+ years hands-on experience in modern C++ on Linux, with strong Git and CMake.
  • Solid image processing and computer-vision foundations (camera models, intrinsics/extrinsics, distortion, PnP, epipolar geometry).
  • Practical experience integrating CV/ML models on device (OpenCV + ONNX Runtime/TensorRT/NCNN/MediaPipe/NPU).
  • Experience building real-time pipelines for live video (GStreamer/FFmpeg, RTSP/RTMP, ring buffers), optimizing for latency & memory.
  • Competence in multithreading/concurrency, lock-free queues, and producer-consumer designs.
  • Knowledge of monocular depth estimation, stereo matching, or structure from motion for distance estimation.
  • Model training exposure (PyTorch/TensorFlow): augmentation, evaluation (precision/recall, ROC/PR), quantization/pruning, conversion to ONNX/TensorRT/NCNN.
  • Cross-compiling, Yocto/Buildroot, containerized toolchains; unit tests (gtest), static analysis (clang tidy, cppcheck), sanitizers.

Benefits:

  • Potential for professional growth and development.
  • A collaborative and dynamic work environment.

Others:

  • Please note that this role requires a high level of technical expertise and problem-solving skills.

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