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

Overview
Job Requirements
Work Experience
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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