I build LLM systems from training and inference to secure, reliable deployment.
Focus: LLM runtimes · GPU/distributed ML · secure execution · robust ML · production systems
Evidence: H100/DDP/FP16-BF16 · FSDP/vLLM · sub-10 ms A6000 inference · Rust sandboxing · 1.8× traffic · 30% lower cost · 60% fewer incidents
Open-source: SGLang · VERL · Megatron-LM · PyTorch AO · LLVM/MLIR · arapuca · ✅ 7 merged
✅ merged — linked counts refresh automatically.
| Repository | PR status |
|---|---|
| LeGambiArt/arapuca | ✅ 2 merged |
| MPSLab-ASU/Seperating_OOD_and_ADV | ✅ 1 merged |
| llvm/llvm-project | ✅ 4 merged |
Auto-updated 2026-09-01 02:14 UTC by update-oss-stats.yml · includes public external PRs authored by amanyagami; excludes personal and excluded repositories
Make Presentation Simple · Nandi RAG assistant · ViT benchmarks · DrDNA
Viyog: Separating Adversarial and Out-of-Distribution — accepted at ESWEEK CODES 2026. Research on separating adversarial and OOD inputs using intermediate-representation geometry.
Resources: Venue · Code · PyPI · Leaderboard · Dataset · Weights · Checkpoints
Education: M.S. Computer Engineering, ASU (2024–2026) · B.Tech Electrical and Electronics Engineering, NITK (2018–2022)
Toolkit: Python · C++ · Rust · Go · PyTorch · CUDA · DDP/FSDP · SGLang · vLLM · Kubernetes · Docker · AWS · Google Cloud
For ML systems, AI infrastructure, or research engineering opportunities: email me or connect on LinkedIn.



