Open to work · Immediately

Ashutosh
Kumar.

Ashutosh Kumar

Backend & Systems Engineer · TypeScript · Go · Rust

I build backend systems that run in production — SaaS platforms with auth and billing, bulk email dispatch engines, video transcoding pipelines. I care about throughput, latency, and correctness; the language is a tool, not the point.

Final-year CS at Manipal University Jaipur. GPA 8.61/10, Dean's List. Graduating July 2026.

8.61
GPA MUJ CS '26 · Dean's List
80★
OSS STARS across all public repos
500+
LEETCODE Java · DSA
35% ↓
API LATENCY Bindisa internship
— FLAGSHIP WORK

Obol

2026

AI API cost tracker — unified spend dashboard for OpenAI, Anthropic, Google, OpenRouter.

Full-stack SaaS on Cloudflare's edge: SvelteKit on Pages, D1 (SQLite) for storage, a standalone cron worker for hourly usage syncs, budget alerts with email + Slack, and AES-256-GCM encryption for every API key at rest.

Obol screenshot
  • Hourly KV-locked cron worker syncs usage incrementally across 4 providers — idempotent upserts prevent double-counting on concurrent deploys
  • daily_rollups pre-aggregation: overview renders from 30 rows, not thousands of usage_records
  • Budget alerts with timezone-aware period math, email + Slack, deduped via last_triggered_at — no double-fire within the same window
  • AES-256-GCM encryption for all API keys and Slack webhooks before insert — ciphertext columns excluded from every list query
  • Magic-link auth (SHA-256 hashed token, 15-min TTL) + KV sessions — zero passwords in the database
  • Constant-time HMAC-SHA256 Lemon Squeezy webhook verification + test-mode guard to prevent silent $0 Pro upgrades
TypeScriptSvelteKitCloudflare D1Cloudflare KVRazorpayResendKeplorKeplorDB

Clairo

2025

Kafka-driven async video transcoding pipeline — S3 → FFmpeg → ECS → multi-res.

A TypeScript pipeline that ingests video upload events via Kafka, fans them out to ECS workers running FFmpeg, and stores 360p / 720p / 1080p outputs back in S3 — a YouTube-style async processing architecture without the SaaS tax.

Clairo screenshot
  • Kafka topic partitioning for parallel multi-resolution transcoding — 360p, 720p, 1080p in one pass
  • ECS task auto-scaling tied to consumer lag — spins up workers on demand, idles at zero cost
  • S3 pre-signed URL workflow for secure upload ingestion and output delivery (no public buckets)
  • Fully async end-to-end: producer acknowledges immediately, consumers commit offsets — zero polling loops
  • ~2.3x faster than sequential transcoding on a 5-min 1080p source (parallel fan-out vs single-worker)
  • Consumer-lag-based autoscaling: 0 → N workers in <90s, scale-down to zero when queue drains
TypeScriptAWS - ECS + ECR + S3 + LambdaApache KafkaFFmpegMySQLNodeJSDockerExpressJSZodPrometheusGrafanaPrisma ORMRedis - Rate Limit

Mailgrid

2025 ★ 26

Offline-first bulk email CLI — CSV, Sheets, SMTP, 10k+ recipients.

A Go CLI for sending personalised HTML email campaigns without SaaS. CSV and Google Sheets input, Go templates, any SMTP server, worker-pool dispatch. Ships as a single static binary for Windows, macOS, and Linux.

Mailgrid screenshot
  • Worker-pool SMTP dispatch with connection reuse — 40% throughput gain over per-send dialing
  • Token-bucket rate limiting (configurable msg/sec + burst) to stay under provider limits
  • Exponential backoff retry that distinguishes transient 4xx from permanent 5xx failures
  • AND/OR/NOT rule engine over CSV columns — cut bounce rate by 10% on real campaigns
  • ~450 emails/sec on commodity hardware · 18 MB resident for 100k-row CSV (streaming)
GoSMTPWorker poolToken bucket - Rate LimitConcurrencySchedulingBoltDB

recon

2026

Token-efficient code intelligence for AI coding agents

MCP server that indexes codebases and gives AI agents symbol-aware answers instead of raw file reads — 15–30× token reduction. Multi-tier search (exact → BM25 → fuzzy → semantic), call graph analysis with PageRank repo maps, incremental indexing via Merkle snapshots and parallel tree-sitter parsing, plus a Cloudflare Worker for auth, billing, and hosted embeddings.

recon screenshot
  • Built a code intelligence MCP server in Rust — tree-sitter (9 langs), SQLite/FTS5, Tantivy BM25. 20 tools, 50K+file repos, 15–30× token reduction for AI agents
  • Multi-tier search pipeline — exact lookup (<1ms) → BM25 → fuzzy (nucleo/FTS5 trigram) → semantic embeddings (768-dim). Reciprocal Rank Fusion for hybrid merging across 4 tiers.
  • Graph analysis engine with custom CSR — bidirectional BFS (O(b^(d/2))), layered call traversal (50K visit cap),Union-Find components, push-based Personalized PageRank touching ~200–500 nodes vs full 50K.
  • Incremental indexing — Merkle snapshots (blake3), parallel parsing via lock-free pools (crossbeam), 250ms debounced watcher, WAL-mode SQLite with 256MB mmap zero-copy reads.
  • Cloudflare Worker API — GitHub OAuth + HMAC-signed licenses, Razorpay billing with webhook verification, 6-bucket rate limiting (fail-closed), KV-cached embedding proxy → Modal T4 GPU (~250ms warm).
  • Lock-free concurrency — ArcSwap caches, DashMap stores, single-flight graph builds, mimalloc. Sub-millisecond responses. Fat LTO + codegen-units=1 release. 8-crate workspace.
Rusttree-sitterSQLite (WAL, FTS5)Tantivy - Fuzzy Search IndexingTokioCloudflareD1 + KV + PagesModalRazorpayMCP - JSON/RPCTypescript
— EXPERIENCE & EDUCATION

Backend Engineer & Team Lead @ Bindisa Agritech

May 2025 – Jul 2025

Remote, India

  • Led a team of 5 engineers designing and shipping backend APIs for an agritech platform — owned core business logic, database schema design, and system architecture.
  • Reviewed and merged 20+ pull requests across the codebase, enforcing modular design patterns, query optimisation standards, and maintainable code practices.
  • Profiled and optimised slow API endpoints — identified N+1 query patterns and missing indexes, reducing p50 response latency from ~320ms to ~210ms (35% cut).
  • Planned and executed 2-week agile sprints with daily standups and sprint retrospectives, delivering all 3 milestones on schedule across the 10-week internship.

B.Tech Computer Science & Engineering @ Manipal University Jaipur

Expected Jul 2026

Jaipur, Rajasthan · CGPA 8.61 / 10.00 · IoT and Intelligent Systems

  • Dean's List — 6th semester (highest GPA)
— BUILT WITH
Production — shipped real work, can interview on it

Go · TypeScript · Java · PostgreSQL · Redis · Docker · Kafka · Linux · Git · Node.js · Express · React · AWS (S3 · ECS · Lambda · EC2 · ECR) · Prisma · MongoDB · Grafana · Prometheus

Comfortable — built things, still sharpening

Python · Tailwind · gRPC · OpenTelemetry · Rust

Exploring — currently reading / building toy projects

Vector databases · RAG pipelines · Kubernetes (in depth) · WebAssembly · eBPF

Milestones
  • Solved 500+ DSA problems on LeetCode (Java)
  • Dean's List — 6th semester, highest GPA
  • Founded @themankindproject — Rust OSS org building systems primitives; imgfprint (159 downloads, 9 versions) and fastarena (135 downloads, 4 versions) published on crates.io
  • 4th / 128 — individual hackathon, MUJ ACM
  • Advanced to finals — Bit to Byte Hackathon, IEEE MUJ
— HOW I BUILD

How I build

Benchmark first

I don't guess about performance — I measure. cargo bench, go test -bench, custom harnesses. If I can't reproduce it, I can't fix it.

Test at the boundary

Unit tests for pure logic, integration tests for IO boundaries (DB, network, filesystem). I test what would actually break in production.

Profile before optimising

flamegraph, pprof, perf — I find the actual bottleneck before touching code. Most "slow" code is fast enough; the 3% that isn't is usually IO or allocation.

Design for failure

Everything fails eventually. I design retries with backoff, circuit breakers, graceful degradation, and explicit error types. Happy paths are easy — edge cases define quality.

Ship small, iterate fast

I prefer small, reviewable PRs over monolithic feature branches. Fast feedback loops catch design mistakes early. If it takes a week to review, the PR is too big.

— ON AI, 2026

I'm a systems engineer in an AI era — not an AI engineer. That distinction matters.

I use AI daily. Claude Code and Copilot have compressed my feedback loop — they handle the boilerplate so I stay focused on system design and tradeoffs. What I don't trust AI for: architecture decisions, correctness guarantees, or anything where the failure mode is silent.

For shipping AI features in production: retries, rate limits, fallbacks, observability — the hard problems still live in the backend. Embeddings are the new indexes. Vector databases are the new Redis. RAG pipelines are the new REST APIs. The AI era needs more systems engineers, not fewer.

My conviction is simple: AI raises the floor, not the ceiling. Correctness, observability, and cost still live at the systems layer. Count me in.

— FOR HIRING TEAMS

Open to full-time roles. Available immediately.

Roles I'm targeting
  • SDE-1 / Junior Backend Engineer
  • Infrastructure / Platform Engineer
  • Systems / Distributed Systems Engineer
  • Developer Tools / DX Engineer
Strong fit if you work on
  • Distributed systems, databases, message queues
  • Developer tools, CLIs, compilers, runtimes
  • Infrastructure, observability, performance
  • LLM inference, RAG, vector search, AI infra
Open to
Full-timeInternshipRemoteIndiaRelocatable

Not looking for: Pure frontend roles, AI research positions, or "full-stack" roles that are 80% UI.

Hiring for backend, infrastructure, or systems engineering? I'm immediately available and ready to ship.

Résumé

or copy: kumarashutosh34169@gmail.com