Architected GPU-accelerated document processing pipeline integrating TensorFlow and PyTorch models for NLU workloads, achieving 65% faster inference through CUDA optimization and mixed-precision (FP16) deployment.
Optimized enterprise ML inference workflows by profiling PyTorch models with Nsight Compute and Python
profilers (cProfile, py-spy), identifying memory bandwidth bottlenecks and reducing batch processing latency by 70%.
Implemented automated performance diagnostics framework for deep learning model serving, reducing P95 response times from 850ms to 390ms through kernel-level profiling and throughput optimization.
Built multi-GPU training pipeline for Virtual Agent NLU models using distributed data parallelism and gradient accumulation, improving training throughput by 3.2x while maintaining 94% model accuracy.
Promoted from GTS Intern to ServiceNow Developer after delivering production ML optimization infrastructure 6 weeks ahead of schedule, exceeding performance benchmarks by 35%
