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Achievement: YOLOAchievement: Pull Sharkx2

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nareshns2004/README.md

💫 Naresh Kumar

AI-ML Infrastructure Engineering

AI Infrastructure & ML Systems Engineer specializing in distributed ML and LLM inference platforms across Kubernetes, GPU-accelerated compute and high-performance networking

I design and operate production-grade systems in Linux, C++ and Python with deep focus on resource scheduling, workload reliability and cluster-scale performance optimization

Currently building distributed inference platforms and GPU orchestration systems, with strong emphasis on observability, fault tolerance and efficient resource utilization


💻 Skills

Programming

C++ Java Python Go C Git Linux Bash

Frameworks

Kafka Hadoop Redis GraphQL TensorFlow pytorch OpenCV Keras

Infrastructure

AWS Docker Kubernetes Jenkins Terraform Ansible Elastic Search OpenStack

💻 Platforms

nareshns2004 nareshns2004

💻 Statistics

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  1. ai-nic-performance-profiler ai-nic-performance-profiler Public

    An observability primitive that closes the attribution gap between NIC hardware counters and distributed training throughput degradation enabling data-driven decisions on fabric topology, RDMA tuni…

    Python

  2. kernel-performance-toolkit kernel-performance-toolkit Public

    A Linux kernel performance analysis toolkit for profiling CPU scheduling, memory behavior, NUMA locality, cache efficiency, page faults and Huge Pages

    Python

  3. distributed-training-framework-nccl distributed-training-framework-nccl Public

    Mini Distributed Training Framework using NCCL

    C++

  4. custom-cuda-fused-attention-triton custom-cuda-fused-attention-triton Public

    Building high-performance GPU kernels from first principles by progressively implementing and optimizing deep learning operators in CUDA and Triton

    Python

  5. disaggregated-inference-engine disaggregated-inference-engine Public

    Python

  6. fault-tolerant-training-orchestrator fault-tolerant-training-orchestrator Public

    A cross-layer reliability system for multi-node LLM training

    Python