Model Development & Optimization: Design, train, validate, and optimize AI/ML models for specific use cases (e.g., signal processing, object detection, predictive analytics).
System Integration: Implement AI solutions into existing or new hardware/software platforms, ensuring real-time performance and scalability.
Performance Analysis: Conduct algorithm benchmarking, tuning, and profiling to maximize accuracy and computational efficiency across platforms (CPU, GPU, edge devices).
Requirements & Skills:
Technical Proficiency: Strong knowledge of AI/ML frameworks (e.g., TensorFlow, PyTorch) and programming languages (e.g., Python, C++).
Mathematical Foundation: Solid understanding of machine learning theory, optimization techniques, and data-driven modeling
Experience in Deployment: Prior experience in deploying AI models on embedded systems or edge devices, with awareness of hardware acceleration and performance constraints.
Strong interpersonal and communication finesse
Proactive problem-solving process
Ability to adapt to evolving technologies and industry best practices
Keen in RF analysis/exploitation and Whitehat hacking technologies
