Backend engineer based in Bangalore 🇮🇳. I learn by building production-grade systems end to end — writing the service, containerizing it, wiring the CI/CD, deploying it, load-testing it, and keeping it observable. Right now I'm pushing deeper into distributed systems, platform / SRE, and AI infrastructure / agentic engineering.

• A production-grade News Feed in Go — auth, post CRUD, a follow graph, hybrid push/pull fan-out, ranking, Redis cache-aside, pgvector semantic search, notifications, and full observability. A real system, not a toy MVP.
• A GitOps homelab on K3s + ArgoCD — moving my self-hosted services to declarative, Git-driven deployments.
• Distributed systems via MIT 6.5840 — MapReduce ✅ · KV server + lock ✅ · Raft up next.

A Go REST API for school management, built as a system-design playground and layered all the way up to production:

• Multi-stage Docker build · GitHub Actions CI/CD to AWS EC2, later redeployed on K3s
• Nginx load balancer across multiple app instances with circuit breaking
• MySQL primary + replica with read/write split · Redis rate limiting + cache-aside
• S3 + CloudFront media storage provisioned with Terraform
• RabbitMQ worker queue · Prometheus metrics · k6 load tests
• Deep-dive docs covering architecture, deployment, observability & load testing

Backend today → platform / SRE and AI-infrastructure / agentic engineering. Same throughline everywhere: own systems end-to-end, and bring that rigor to the infrastructure that runs AI. Next on the roadmap — a real-time Chat service (WebSockets + WebRTC) and an LLM agent in TypeScript with LangGraph and a proper eval harness.

Languages

Backend & Data

Infra & DevOps

Observability & Testing

• 💼 LinkedIn — omkar-shendge
• 📧 Email — omkarshendge619@gmail.com
• 🐦 X / Twitter — @p0pobeast