Description

Key Skills: C, C++, Python, Shell Scripting, AI Model Inference, AI Compiler, TVM, TensorRT, XLA, Glow, OpenVINO, Quantization, AI Deployment, Embedded Systems, ONNX, PyTorch, TensorFlow, FPGA, Runtime Optimization, Performance Profiling, Embedded Boards

Roles & Responsibilities:

• Develop and optimize AI model inference pipelines for embedded platforms
• Work on AI model compilation, runtime execution, and embedded software stack integration
• Optimize performance, latency, and resource utilization for AI workloads on embedded devices
• Integrate and work with AI compilers such as TVM, TensorRT, XLA, Glow, or OpenVINO
• Implement and evaluate quantization techniques and accuracy-performance tradeoffs
• Debug and resolve performance bottlenecks across compiler, runtime, and model layers
• Support AI deployment stacks and optimize inference execution workflows
• Work with ONNX, PyTorch, and TensorFlow model export and inference pipelines
• Run, profile, and benchmark AI models on embedded boards and FPGA platforms
• Analyze profiling and performance data to improve runtime efficiency and model behavior
• Collaborate with cross-functional engineering teams for embedded AI solution development
• Maintain technical documentation and support continuous optimization initiatives

Experience Required:

• 4+ years of experience in Embedded AI, AI Compiler, or AI Runtime Engineering
• Strong proficiency in C, C++, Python, and Shell Scripting
• Deep understanding of AI inference flow from model compilation to runtime execution
• Hands-on experience with AI compilers such as TVM, TensorRT, XLA, Glow, or OpenVINO
• Experience with quantization and inference optimization techniques
• Knowledge of ONNX, PyTorch, and TensorFlow inference export pipelines
• Hands-on experience with embedded boards or FPGA platforms
• Strong analytical, debugging, and performance optimization skills

Education: Any Graduation

Key Skills

Education

Any Graduate