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2 Days ago

Senior Software Engineer – EDGE AI Development

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Mumbai, Maharashtra, India
Information Technology
Full-Time
Quest Global

Overview

Job Requirements

Key Responsibilities

Design and Development:

  • Design and implement AI/ML-based applications tailored for embedded and edge hardware.
  • Develop end-to-end pipelines for model training, conversion, and deployment.
  • Customize neural network architectures for edge-specific applications such as object detection, classification, and segmentation.

Model Optimization and Deployment:

  • Port and optimize AI models to meet performance and memory constraints on edge platforms.
  • Apply quantization (e.g., INT8), pruning, and layer fusion techniques to improve model efficiency.
  • Convert models between various formats such as ONNX, TensorRT, TVM, TFLite, and DRP-AI.

Performance Tuning and Profiling:

  • Analyze model bottlenecks and tune for latency, throughput, and power efficiency.
  • Run inference performance profiling on hardware targets and iterate for improvements.

Testing and Debugging:

  • Validate model accuracy and performance post-optimization.
  • Debug and troubleshoot model behavior discrepancies across frameworks and devices.

Documentation and Research:

  • Maintain documentation for all model lifecycle stages and optimization steps.
  • Stay updated with latest AI compiler advancements and deployment trends in edge AI.

Work Experience

Must Have:

  • Bachelor's/Master’s degree in Computer Science, Electronics, or AI-related field.
  • 6+ years in AI/ML model development with experience in real-world applications.
  • Proficient in Python, C++ and deep learning libraries (TensorFlow, PyTorch, Keras).
  • Solid understanding of CNNs, FCNs, and their applications in computer vision.
  • Practical knowledge of model optimization workflows (quantization, pruning, etc.).
  • Experience with ONNX, TVM, TensorRT, DRP-AI, TFLite, OpenCV, etc.
  • Experience with deployment on edge devices like Jetson, RZ/V2H, or STM32.
  • Strong understanding of constraints (compute, memory, power) in edge environments.

Good to Have:

  • Exposure to embedded Linux or RTOS environments.
  • Familiarity with low-level model debugging, calibration tools, and inference engines.
  • Experience with Continuous Integration tools such as Git, Jenkins, or similar.
  • Understanding of hardware accelerators (GPU, NPU, DRP-AI, etc.).
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