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.
| 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 |
- 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)
- 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
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)
- 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



