Role: AI/ML Engineer (Computer Vision)

Role Summary

We are looking for a highly motivated AI/ML Engineer to join our team. In this role, you will be responsible for designing, training, and optimizing deep learning models for complex computer vision tasks. The ideal candidate will bridge the gap between model research and production deployment, ensuring that our vision solutions are high-performing, scalable, and optimized for real-time inference on edge devices.

Key Responsibilities

Model Development: Design, train, and optimize deep learning models for computer vision applications, including object detection, image classification, and semantic segmentation.
Optimization & Fine-Tuning: Fine-tune state-of-the-art architectures (e.g., CNNs, Vision Transformers) using frameworks like PyTorch and TensorFlow.
Performance & Quantization: Apply advanced model optimization techniques, including model quantization, to balance precision and performance.
Data Strategy: Implement effective pre- and post-processing techniques, as well as data augmentation strategies, to maximize model accuracy and robustness.
Deployment & Integration: Deploy Deep Learning models in C++ environments using TorchScript, ONNX, and TensorRT. Integrate these models into production systems, including APIs, data pipelines, and edge devices.
Performance Engineering: Optimize models for performance, scalability, and real-time inference requirements.

Required Qualifications

Education: Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related field.
Experience: 1–2 years of hands-on experience in Deep Learning and Computer Vision.
Programming Proficiency: Expertise in Python and C++(Mandatory).
Technical Skills:

Strong familiarity with deployment frameworks (TorchScript, ONNX, TensorRT).
Practical experience in model quantization and hardware-level performance tuning.

Soft Skills: Strong logical reasoning, analytical problem-solving skills, and a demonstrated ability to work collaboratively in a cross-functional team.

Preferred Qualifications

NVIDIA Ecosystem: Experience building and deploying inference pipelines using NVIDIA technologies, including NGC models, TAO Toolkit, DeepStream Metropolis, and Triton Inference Server.
Hardware Acceleration: Experience with GPU acceleration (CUDA).
Domain Expertise: Proven background in Industrial Computer Vision applications.
Industry Awareness: Commitment to staying current with the latest industry trends and emerging technologies in the AI/ML space.

AI/ML Engineer (Computer Vision)(8)

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