Backend engineer · Kafka, IoT, distributed systems. I write long-form posts the same way I debug: what broke first, what tradeoff you pay, what shipped in production.
At work: 10k+ WebSocket connections, Kafka-scale pipelines, ClickHouse / Mongo / Redis — real constraints, not demos. I also keep DSA sharp with LeetCode (~1,000 problems solved · contest rating ~1,577 (~top 27% globally)).
Coding practice: @Amitverma2216 — ~1,000 problems solved · contest rating ~1,577 (~top 27% globally). Same discipline as production: ship often, measure, iterate.
~3 years in production backend roles
What I ship with most often. Deeper on Node/TS; honest exposure on the rest.
If your JD mentions Kafka, polyglot databases, AWS, WebSockets, or strong DSA alongside backend work — that’s the lane I fit.
Node/TS in production; Go for services, tooling, and performance-sensitive pieces.
REST + sockets under load.
Right store for the job.
Events, fan-out, backpressure.
AWS-first, repeatable deploys.
Know when things fail.
Wiring LLMs into real systems — not demos.
Consistent problem solving — keeps algorithms and data structures sharp next to production work.
I'm a Software Engineer with about three years building production distributed systems. I work at a Stealth Startup (Gurugram, India), where I architect real-time data pipelines with Kafka, multi-database strategies across MongoDB, ClickHouse, and PostgreSQL, and high-concurrency WebSocket infrastructure for IoT and NOC-style platforms — including work that serves enterprise deployments for clients such as Reliance and Altius Infra.
Previously I shipped notification systems handling 1M+ daily notifications at JP Furnware & Technologies, and AI-powered SaaS at PinnacleWorks Infotech. I hold a B.Tech in Computer Science from CCS University (CGPA 8.29/10).
I keep data structures & algorithms current with steady LeetCode practice (~1,000 problems solved · contest rating ~1,577 (~top 27% globally)) — it rounds out production engineering with measurable problem-solving depth.
This site exists because most engineering content shows you the clean answer. I show you the seven approaches that failed first — the real thinking, the edge cases, and the honest tradeoffs you have to make at scale.
Also in the mix when the product needs it: Prisma, React, Airflow — not the headline, but real shipping work.
Hiring, collaboration, speaking, or a question about something I wrote — send an email. I read everything.
amit.verma.codes@gmail.comUses your default mail app (Gmail, Outlook, Apple Mail, etc.) — no form, no tracking.
A user had access to 1 lakh devices across MongoDB, ClickHouse, and Elasticsearch. We tried seven approaches. All broke. The full journey — dead ends, edge cases, and what production systems actually do.
How similarity search works under the hood — and why naive brute-force collapses at scale.
What happens when your index outgrows your data — and how we fixed it without downtime in production.
Fan-out on write vs fan-out on read — and why Twitter uses a hybrid at 500M users. A beginner-friendly breakdown.
A production case study — how a small config issue brought down an entire IoT device fleet and what fixed it.
More essays on Medium — same voice: failures first, tradeoffs explicit, production-shaped problems only.