AI / ML Engineer

Job Title:

AI/ML Engineer Quantization & Optimization for CNN Models

Job Summary:

We are seeking a highly skilled AI/ML Engineer with deep expertise in quantization, optimization, and deployment of CNN-based models for efficient edge and embedded inference. The ideal candidate will have strong experience with Post-Training Quantization (PTQ), Quantization-Aware Training (QAT), and mixed-precision inference, coupled with practical knowledge of deploying models using Qualcomm SNPE/QNN SDK/NVIDIA TensorRT framework. This role involves developing scalable and efficient CNN solutions for diverse applications such as object detection, image classification, segmentation, and embedded vision systems.

Key Responsibilities:

• Design, implement, and fine-tune quantization pipelines for CNN architectures including ResNet, MobileNet, YOLO, and EfficientNet etc.
• Apply PTQ and QAT techniques to minimize accuracy degradation while optimizing for low-latency and low-power inference.
• Conduct layer-wise sensitivity analysis, activation calibration, and mixed-precision tuning.
• Develop and maintain the toolchain for CNN deployment using frameworks such as PyTorch.
• Perform benchmarking and profiling to evaluate trade-offs between accuracy, latency, and power efficiency.

Required Skills & Experience:

• Strong knowledge of CNNs and their optimization workflows.
• Proficient in PyTorch/TensorFlow.
• Familiarity with SNPE/QNN SDK/TensorRT/OpenVINO for hardware-aware deployment.
• Solid programming skills in Python; working knowledge of C++ preferred.
• Experience with model compression, pruning, and mixed-precision training.