LLM inference engineer. I get models running 3× faster on hardware you already own.
ramaseshanms.github.io · LinkedIn · m.s.ramaseshan@gmail.com
| Result | Context |
|---|---|
| 300% throughput on NVIDIA Jetson Orin/Xavier | CUDA kernel optimization — directly enabled a Flash LIDAR partner to close a Rivian production contract |
| 3.3× model compression, −1.06% perplexity | Q4_HQQ quantization natively into llama.cpp · 151 tok/s on CPU inference |
| ~3× visual SLAM speedup on constrained edge hardware | ORB-SLAM3 memory architecture redesign for TI TDA4VM DSP via ROS2 |
| Production AV perception in Autoware Universe | BEVDet/BEVFormer real-time multi-sensor fusion with deterministic latency |
| Project | What it does | The number that matters |
|---|---|---|
| Viking | Vulkan compute LLM runtime for Android | 9 quantization shaders · 37 security fixes shipped |
| llama.cpp Q4_HQQ | Native HQQ quantization with AVX2/NEON SIMD kernels | 151 tok/s · 5.99 GiB → 1.79 GiB · 45% faster GGUF conversion |
| ORB-SLAM3 Edge | Visual SLAM ported to DSP hardware, memory pipeline redesigned | ~3× speedup on TI TDA4VM |
| EnterpriseGradeRAG | Production RAG system with role-based access control | 167 unit tests · multi-tenant |
| BEV Perception | BEVDet/BEVFormer integrated into Autoware AV stack | Shipped to production autonomous vehicle hardware |
| DeepCache | Adaptive KV cache tiering between GPU VRAM and host RAM | In progress |
Kernel / Hardware: CUDA · Vulkan Compute · GLSL/SPIR-V · AVX2/AVX-512 · ARM NEON · OpenCL
Languages: C++20 · Python · Bash · ARM Assembly · Kotlin
Inference: llama.cpp · vLLM · ONNX Runtime · PyTorch · TensorFlow Lite
Platforms: AMD EPYC (NUMA, multi-socket) · NVIDIA Jetson Orin/Xavier · TI TDA4VM · Qualcomm Adreno
Tools: CMake · ROS2 · Autoware Universe · Docker · Perfetto · ZenDNN
I publish technical deep-dives on LLM quantization, Vulkan optimization, edge inference, and ARM porting — ramaseshanms.github.io.
The intersection of fast inference and agentic AI is where I'm focused. Multi-step agents are only as capable as their underlying compute is fast and cheap — and that's a kernel-level problem. Actively learning, building, and looking for problems in this space.
Founding engineer and AI infrastructure roles.
Best fit: teams where inference speed is a real constraint — edge AI, on-device LLMs, autonomous systems, agentic systems, or serious LLM serving infrastructure. I work best when the hardware problem is the product problem.