Profile
I work on systems where latency, transport, reliability, and product pressure all matter at the same time. The resume behind this site spans blockchain protocols, self-hosted platforms, large-scale inference backends, and production AI tooling.
05
Core systems shipped
300+
Devices validated in mesh trials
40%
API cost reduction achieved
Operating Style
I prefer system problems where product and infrastructure meet.
That usually means protocols with adversarial behavior, APIs under cost pressure, deployments under time pressure, or multi-service systems that need graceful failure handling instead of happy-path demos.
Offline-first thinking across BLE, NFC, Wi-Fi Direct, and QR transports.
Async backends with Redis, PostgreSQL, Node.js, Python, Docker, and Nginx.
Security-oriented design using deterministic settlement and replay protection.
Perspective
I like engineering problems where the network is unreliable, the product still has to feel clean, and the failure mode actually matters.
ADARSH GUPTA
Profile Snapshot
Title
Backend & Distributed Systems Engineer · Blockchain · AI Systems
Phone
+91 70339 03639Education
BTech in Computer Science · SRM University AP · Jul 2024 – May 2028
Capability Areas
Capability Areas
Blockchain & Cryptography
Trustless protocol design
EVM, UTXO, escrow, settlement
Designing systems where transaction validity, replay protection, and eventual settlement are first-class constraints rather than follow-up hardening tasks.
Backend & Distributed Systems
Infrastructure with delivery pressure
Node.js, Python, Postgres, Redis, Docker
Building backends that handle concurrency, retries, queues, billing, state, and integrations without pushing reliability problems into operations.
Networking & Systems
Transport-aware engineering
BLE, NFC, Wi-Fi Direct, DTN, Nginx, Linux
Working close to transport and system behavior when performance or disconnection risk makes generic web assumptions insufficient.
Execution
Fast shipping without thin foundations
Founding, prototyping, productionization
Moving from prototype to real usage quickly, then tightening the architecture until it behaves predictably under scale, cost, and product iteration.