AI Software Development Engineer

4 days ago

Ghaziabad, Uttar Pradesh, India beBeeSoftware Full time ₹ 15,00,000 - ₹ 25,00,000

About an Industrial Automation Company

  • Our software engineers design and develop C++ modules for video capture, preprocessing, inference, and post-processing on Linux.
  • They implement image processing pipelines and computer vision algorithms, including denoise, resize, color space conversion, undistortion, keypoints detection, homography estimation, optical flow tracking, and object tracking.

To achieve this, we require professionals who can build and optimize distance estimation from monocular or stereo cameras using calibration, geometry, and/or depth-estimation networks.

The Key Responsibilities:
  • We need engineers to integrate machine learning models (PyTorch/TensorFlow -> ONNX/TensorRT/NNAPI/NPU runtimes) for DMS/ADAS events, including drowsiness detection, distraction/gaze monitoring, phone usage detection, smoking detection, seat belt enforcement, etc.
  • Meet real-time targets (FPS/latency/memory) on CPU/GPU/NPU using SIMD/NEON, multi-threading, zero-copy buffers.
  • Write clean, testable C++ code, maintain CMake builds and Git-based workflows.
  • Instrument logging/telemetry, debug with gdb/addr2line, sanitize and profile with perf/valgrind.
  • Collaborate with data/ML teams on dataset curation, labeling specs, training/evaluation, and model hand-off.
Required Skills and Qualifications:
  • A minimum of 2+ years hands-on experience in modern C++ on Linux, with strong Git and CMake skills.
  • Solid image processing and computer vision foundations, including camera models, intrinsics/extrinsics, distortion, PnP, epipolar geometry.
  • Practical experience integrating CV/ML models on device (OpenCV + ONNX Runtime/TensorRT/NCNN/MediaPipe/NNAPI).
  • 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.
  • Comfort with debugging & profiling on Linux targets.
  • 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.
  • Hardware acceleration (GPU/VPU/NPU, Arm NEON/DSP), YOLO/RT DETR/Lightweight backbones on edge.
  • Cross compiling, Yocto/Buildroot, containerized toolchains; unit tests (gtest), static analysis (clang tidy, cppcheck), sanitizers.
  • Basic familiarity with MQTT/IoT, message schemas, and over-the-air updates.

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