Engineer, Principal/Manager - Machine Learning, AI

model inference, optimization, debugging, and hardware acceleration

cutting-edge research

both applied AI development and AI research

• Education & Experience:

• 20+ years of experience in AI/ML development, with at least 5 years in model inference, optimization, debugging, and Python-based AI deployment.
• Masters or Ph.D. in Computer Science, Machine Learning, AI.

• Leadership & Collaboration:

• Lead a team of AI engineers in Python-based AI inference development.
• Collaborate with ML researchers, software engineers, and DevOps teams to deploy optimized AI solutions.
• Define and enforce best practices for debugging and optimizing AI models.

• Key Responsibilities:

• Model Optimization & Quantization:

  Optimize deep learning models using quantization (INT8, INT4, mixed precision etc), pruning, and knowledge distillation.
• Implement Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT) for deployment.
• Familiarity with TensorRT, ONNX Runtime, OpenVINO, TVM.

• AI Hardware Acceleration & Deployment:

  Optimize AI workloads for Qualcomm Hexagon DSP, GPUs (CUDA, Tensor Cores), TPUs, NPUs, FPGAs, Habana Gaudi, Apple Neural Engine.
• Leverage Python APIs for hardware-specific acceleration, including cuDNN, XLA, MLIR.
• Benchmark models on AI hardware architectures and debug performance issues.

• AI Research & Innovation:

  Conduct state-of-the-art research on AI inference efficiency, model compression, low-bit precision, sparse computing, and algorithmic acceleration.
• Explore new deep learning architectures (Sparse Transformers, Mixture of Experts, Flash Attention) for better inference performance.
• Contribute to open-source AI projects and publish findings in top-tier ML conferences (NeurIPS, ICML, CVPR).
• Collaborate with hardware vendors and AI research teams to optimize deep learning models for next-gen AI accelerators.

• Details of Expertise:

• Experience optimizing LLMs, LVMs, LMMs for inference.
• Experience with deep learning frameworks: TensorFlow, PyTorch, JAX, ONNX.
• Advanced skills in model quantization, pruning, and compression.
• Proficiency in CUDA programming and Python GPU acceleration using cuPy, Numba, and TensorRT.
• Hands-on experience with ML inference runtimes (TensorRT, TVM, ONNX Runtime, OpenVINO).
• Experience working with RunTimes Delegates (TFLite, ONNX, Qualcomm).
• Strong expertise in Python programming, writing optimized and scalable AI code.
• Experience with debugging AI models, including examining computation graphs using Netron Viewer, TensorBoard, and ONNX Runtime Debugger.
• Strong debugging skills using profiling tools (PyTorch Profiler, TensorFlow Profiler, cProfile, Nsight Systems, perf, Py-Spy).
• Expertise in cloud-based AI inference (AWS Inferentia, Azure ML, GCP AI Platform, Habana Gaudi).
• Knowledge of hardware-aware optimizations (oneDNN, XLA, cuDNN, ROCm, MLIR, SparseML).
• Contributions to open-source community.
• Publications in International forums conferences journals.