AI Engineer Model Optimization & Acceleration

Role Overview

Seeking an AI Engineer to optimize and deploy ML models across heterogeneous platforms (CPU, GPU, NPU).
Work on scalable, production-ready AI systems across domains like robotics, healthcare, and automotive.

Key Responsibilities

• Optimize diverse models: generative (LLMs, diffusion), vision (classification, detection, segmentation), CPU,NPU,GPU,AI,multi-modal,speech,LLMS,PyTorch,ONNX,runtimes,GNU,NPU,FP32,debug,lower precision
• Port models across frameworks (e.g., PyTorch ONNX runtimes)
• Deploy on hardware accelerators (GPU/NPU) and optimize performance
• Improve inference latency, throughput, and memory (batching, caching, parallelism, fusion)
• Apply quantization and model compression (FP32 lower precision)
• Profile and debug system and model performance

Required Skills

• Strong in PyTorch (or similar), ONNX (or equivalent)
• Proficient in Python and C++
• Experience with GPU/hardware acceleration (CUDA,ROCm or similar)
• Solid understanding of deep learning models (transformers, CNNs)
• Knowledge of optimization, quantization, and performance tuning

Good to Have

• Edge AI or embedded deployment
• Generative or multi-modal AI systems
• Distributed inference or streaming pipelines

Experience

710 years

• Role & responsibilities

Preferred candidate profile

Perks and benefits