AI infrastructure engineer. GPU serving on Kubernetes, LLM inference runtimes, and MLPerf Inference benchmarking.
Most of my day job lives in private repositories, so this page tracks what I have been able to contribute upstream.
Merged
| NVIDIA/TensorRT-LLM#18865 | Warn when a tool parser detects markup but extracts no tool calls | a silently dropped tool call now leaves a diagnostic naming the configured parser |
| NVIDIA/TensorRT-LLM#18856 | Cover /v1/responses in per-request perf metrics tests |
closed the regression gap left by an earlier fix |
| NVIDIA/deepops#1397 | Run nvidia-smi tasks outside the ssh cgroup |
GPU tasks no longer die with the login session that started them |
| NVIDIA/deepops#1394 | Do not replace the DCGM that DGX OS already ships | |
| NVIDIA/deepops#1390 | Prefer in-tree nvidia_peermem over legacy nv_peer_mem |
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| NVIDIA/deepops#1389 | Use ssh.service on Debian-family when hiding GPUs from login shells |
In review
| vllm-project/vllm#56582 | Keep a plain CUDA arch requested alongside its a/f variant |
sm_100a-only kernels failed to load on B300 (CC 10.3) |
| NVIDIA/gpu-operator#2870 | Configurable ServiceAccount for the DCGM Exporter | lets IRSA / Workload Identity bind to the operator-managed exporter |
| mlcommons/inference#2668 | Bound and pre-load the DeepSeek-R1 LiveCodeBench grader workers | a grader that OOM-died was scoring its own failure as a wrong answer |
| mlcommons/inference#2669 | Pin anthropic<1.0 in the DeepSeek-R1 evaluation requirements |
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| mlcommons/inference#2670 | Fix the llama3.1-8b Offline vLLM SUT on current vLLM | |
| mlcommons/inference#2671 | Make the llama3.1-8b run scripts and README accuracy flow runnable | |
| mlcommons/inference#2672 | Default --audit-conf to audit.config in the reference entry points |
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| kubeflow/trainer#4045 | Do not allocate a worker when runLauncherAsNode is set |
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| kubeflow/katib#2720 | Keep the skopt suggestion service from stalling and OOMing |
- GPU serving on Kubernetes — operators, autoscaling (KEDA), multi-instance GPU, DCGM metrics
- LLM inference runtimes — TensorRT-LLM and vLLM, quantization (FP8 / NVFP4), disaggregated serving
- MLPerf Inference — benchmarking and submission pipelines on NVIDIA B300