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mukhriddin-dev/README.md

Mukhriddin Khodieff

Senior Software Engineer · Go & Platform Engineer · AI Infrastructure

I design and run high-performance backends in Go, build cloud-native platforms,
and deploy bare-metal GPU clusters (NVIDIA HGX B200) for LLM training & inference.


⚡ Core Focus

Area What I do
Go Backend High-load microservices, gRPC APIs, event-driven systems, low-latency services
Platform / DevOps / SRE Kubernetes platforms, GitOps, IaC, observability, zero-downtime delivery
AI Infrastructure HGX B200 cluster bring-up, GPU networking, LLM inference & training stacks
Full-stack React / Next.js / Vue frontends on top of the platforms I build

🐹 Go Engineering

  • Frameworks & libs: Gin, Echo, Fiber, Chi, net/http (std), gRPC + Protobuf, Connect-RPC
  • Data: pgx, sqlc, GORM, Ent, go-redis, MongoDB driver
  • Messaging & streaming: Kafka (franz-go, sarama), NATS / JetStream, RabbitMQ
  • Architecture: Clean / Hexagonal architecture, DDD, CQRS, Event Sourcing, Saga, Outbox pattern
  • Concurrency: goroutines & channels, worker pools, context propagation, rate limiting, backpressure
  • Performance: pprof, trace, benchmarks, escape analysis, memory/GC tuning
  • Reliability: circuit breakers, retries with jitter, idempotency, graceful shutdown
  • Testing: testify, gomock, testcontainers-go, fuzzing, table-driven tests
  • Tooling: Go modules & workspaces, golangci-lint, Wire / Fx (DI), Cobra CLIs, Kubernetes operators (controller-runtime, Kubebuilder)

☸️ DevOps · Platform Engineering · SRE

  • Containers & orchestration: Docker, Kubernetes (kubeadm, k3s, EKS/GKE), Helm, Kustomize
  • GitOps & CI/CD: Argo CD, Flux, GitHub Actions, GitLab CI, Argo Workflows / Rollouts (canary, blue-green)
  • Infrastructure as Code: Terraform / OpenTofu, Ansible, Packer, cloud-init
  • Networking: Cilium (eBPF), Calico, Istio / Linkerd, Ingress-NGINX, Traefik, MetalLB, HAProxy, Envoy
  • Observability: Prometheus, Grafana, Loki, Tempo, OpenTelemetry, Alertmanager, VictoriaMetrics
  • Storage: Ceph / Rook, Longhorn, MinIO (S3), NFS, local NVMe
  • Security: Vault, cert-manager, external-secrets, Trivy, Kyverno / OPA, RBAC, network policies, SSO (Keycloak)
  • SRE practices: SLO/SLI, incident response, postmortems, capacity planning, chaos testing
  • Linux: kernel & sysctl tuning, systemd, networking (iproute2, bonding, VLANs), bash automation

🔥 AI Infrastructure — NVIDIA HGX B200 Clusters

End-to-end bring-up of bare-metal GPU servers: from rack to running LLM endpoints.

Hardware & bring-up

  • NVIDIA HGX B200 (8× Blackwell GPU, 192 GB HBM3e each, 5th-gen NVLink / NVSwitch)
  • Rack planning: power (high-density PDUs, ~14 kW+ per node), cooling (air / direct liquid cooling), cabling
  • BMC / IPMI / Redfish, BIOS & firmware updates, PXE / MAAS bare-metal provisioning
  • Burn-in & validation: DCGM diagnostics, nvidia-smi, NCCL tests, HPL, GPU stress tests

GPU networking

  • InfiniBand NDR 400G & RoCEv2 (Spectrum-X), NVIDIA ConnectX-7 / BlueField-3 DPU
  • Rail-optimized fat-tree topology, UFM, Subnet Manager, GPUDirect RDMA & GPUDirect Storage
  • NCCL tuning for multi-node all-reduce performance

Software stack

  • NVIDIA drivers, CUDA 12.x, Fabric Manager, NVIDIA Container Toolkit
  • Kubernetes GPU Operator, Network Operator, MIG partitioning, time-slicing
  • Scheduling: Slurm (Pyxis / Enroot), Kueue, Volcano, Run:ai concepts
  • Monitoring: DCGM Exporter → Prometheus / Grafana (temps, power, ECC, XID errors, NVLink health)
  • High-performance storage: WEKA / VAST / Lustre concepts, NVMe-oF

LLM serving & training

  • Inference: vLLM, SGLang, TensorRT-LLM, NVIDIA Triton, NVIDIA NIM, llama.cpp, Ollama
  • Optimizations: FP8 / FP4 quantization, tensor & pipeline parallelism, continuous batching, KV-cache tuning, speculative decoding
  • Distributed training: PyTorch DDP / FSDP, DeepSpeed, Megatron-LM, LoRA / QLoRA fine-tuning
  • MLOps: Ray, KServe, Kubeflow, MLflow, LiteLLM gateway (OpenAI-compatible APIs)

🤖 AI / LLM Application Engineering

  • LLM APIs: Anthropic Claude, OpenAI, Gemini, open-weight models (Llama, Qwen, DeepSeek, Mistral)
  • RAG pipelines, embeddings, hybrid search, reranking
  • Vector DBs: pgvector, Qdrant, Milvus, Weaviate
  • AI Agents, tool calling, MCP (Model Context Protocol) servers in Go & TypeScript
  • LangChain, LangGraph, LlamaIndex, Vercel AI SDK
  • Evaluation & observability: Langfuse, LangSmith, OpenTelemetry for LLMs

🎨 Frontend
  • TypeScript, React 19, Next.js 15 (App Router, RSC), Vue 3, Nuxt 3
  • TanStack Query / Router, Redux Toolkit, Zustand, GraphQL (Apollo)
  • TailwindCSS v4, shadcn/ui, Radix, MUI, Ant Design
  • Testing: Vitest, Jest, React Testing Library, Playwright, Cypress
🧩 Other Backend
  • Node.js, NestJS, Express, Hono, Bun
  • PHP 8.3+, Laravel 12, Yii2
  • MySQL, Oracle, PostgreSQL, MongoDB
  • Prisma, Drizzle ORM
📱 Mobile
  • React Native, Expo
  • Swift, SwiftUI
  • Ionic

📊 GitHub Stats

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    Vite & Vue powered static site generator.

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  4. Go-blog-app Go-blog-app Public

    A blog site structure written in Go

    Go

  5. Vue-Js-cheat-sheets Vue-Js-cheat-sheets Public template

    Get the Ultimate Vue Cheat Sheet 2023 (PAGE) PDF DOWNLOAD

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    Nuxt-WooCommerce v3 | GrapQl

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