Fig. 00 / subject · AI/ML engineer, Bengaluru

Nishanth Antony.

I build AI that has to work on Monday: vision-LLM graders that land within one mark of the teacher, agents that chase failed payments without spamming anyone, and the unglamorous backend underneath. Unfamiliar stack? I learn it on the way.

Fig. 00b / learning ledger[SINCE 2024]

every stack I picked up because something had to ship. 18 in two years, about one every six weeks.

2026-10 · 16 / 16

Postgres row-level security

because one school's fees are not another school's business. Kwilo admissions and fees.

Fig. 01 / shell [LIVE]

nishanth-lab shell 1.0. type , or tap a command.

try:

tab completes · ↑↓ history · esc leaves

location: Bengaluru, IN · UTC+5:30
availability: [OPEN TO: AI/ML ROLES, 2027]
current focus: Kwilo AI · open-aiops · Dhatri
last updated: 2026-10-14

§01 / work

Selected work

6 entries. each states the problem, the approach, the result, and what it cost me to learn.

Fig. 02 / project / 2026[SHIPPED]

Kwilo AI · production research

Vision-LLM exam grader

problemMarking handwritten university scripts eats teachers' evenings, and a model that is two marks off per subpart is worse than no model.

approachScanned PDF to page images, pages mapped to each subpart, then a vision LLM reads the marking scheme plus a few teacher-marked exemplars. Benchmarked GPT-5.4 Mini, Gemini 3 Flash and 3.1 Pro over 4 subjects and 11 test cells.

python
azure openai
vertex ai
pymupdf
uv
matplotlib

pooled MAE, marks per subpart (few-shot subparts): 0.855
error vs zero-shot, GPT (1.259 to 0.910): −28%
subparts within ±1 mark of the teacher: 81.7%

what I learnedExemplars beat model size: few-shot Flash (0.890) beat zero-shot Pro (1.195). And a harness that writes run manifests is worth more than any single prompt tweak, because it turns "feels better" into a number.

show the pipeline

Fig. 02a. One subpart per call in the subparts pipeline; that variant had zero script failures.

Fig. 03 / project / 2026[SHIPPED]

Razorpay AI Buildathon 2026 · track 03

PayRecover

problemA failed UPI or card payment can't be silently re-charged. Merchants either spam the customer or lose the sale.

approachRules diagnose the failure; Gemini only sees the ambiguous ones. Every action (wait, link, remind, escalate, stop) passes a policy gate with caps and a kill switch, and lands in an append-only audit log.

python
razorpay api
gemini
sqlite
pytest

of failed revenue recovered (INR 44,205 of 141,353): 31.3%
of recoverable customers captured: 76.1%
seeded failure cases, one dry run: 80

what I learnedThe model call is the easy 10%. The agent is the policy around it: typed timeouts, writes that can't double-fire, and a stop that stays stopped after you flip the switch back.

show the loop

Fig. 03a. The LLM sits inside "diagnose". It never gets to skip the gate.

Fig. 04 / project / 2026[IN PROD]

Kwilo AI · Apr–May, Jul 2026–now

Shipping on an AI learning platform

problemSchools run exams, fees and admissions on this product, so a bug costs real money or leaks a real parent's phone number.

approachAnswer sheets auto-matched to students (filename first, then header OCR, never blocking the upload). A public blog stack end to end. Fee receipts with race-safe installment settlement. Admissions with a primary guardian and a one-page A4 form.

fastapi
sqlalchemy async
postgres + RLS
alembic
react 19
typescript
tanstack query
gemini

uploads blocked when OCR can't read a sheet: 0
product areas: exams, content, blog, fees, admissions: 5
guardians per admission, one becomes the parent login: 2

what I learnedA self-reported phone number is not proof of identity, so admitting a student now asks staff before linking a parent account. And lock the row before two payments settle the same installment milliseconds apart.

kwilo.ai ->
code: private

Fig. 05 / research / 2026[RESEARCH]

Research project · IoT security · paper on the way

Federated IoT anomaly detector

problemIoT networks need intrusion detection, but shipping raw traffic from every camera and sensor to one server is a privacy and bandwidth problem.

approachDockerised federated clients for cameras, sensors and controllers; RFRE preprocessing on IP and port features; FedAvg vs FedProx aggregation benchmarked for stability against spoofing and DDoS.

python
pytorch
docker
fedprox
scikit-learn

aggregated AUC with FedProx: 0.98
device classes simulated: 3
raw packets leaving a client: 0

what I learnedMost of what I called "model instability" was a data-partitioning bug. Check the shards before you tune the optimizer.

progress report (pdf) ->
IEEE paper: in preparation

Fig. 06 / open source / 2026[OSS]

Intervues Club · open-aiops cohort · Apache-2.0

open-aiops governance

problemEnterprise LLM apps die on a single provider outage or a 429. Callers shouldn't rewrite code every time a model flakes.

approachI own governance/ in the open-source AI ops control plane: a model router that ranks providers by cost, latency and overload, walks a ranked list for failover on rate limits and timeouts, and retries with exponential backoff plus full jitter. Public PRs, reviewed by someone else.

python
pydantic
fastapi
pytest
circuit / failover
open source

merged PRs I own on governance: 4+
modules: router, failover, retry, config import: 4
real API keys in CI: 0

what I learnedPer-request fallback must walk a ranked decision list. Marking a provider "overloaded" on shared router state would poison other in-flight requests. Small PRs with fake providers beat one giant "add LiteLLM" dump.

Fig. 07 / hackathon / 2026[SERVERPOD]

Serverpod hackathon · Lazy Monks · in progress

Dhātrī care companion

problemElders in India manage chronic meds while adult children live elsewhere. A missed dose at 8 PM shouldn't wait for the next family call.

approachFlutter app (patient / caregiver / doctor) on a Serverpod backend: prescription photo → Gemini draft schedule → human confirm → Future Calls for reminders and escalation → live caregiver alerts over WebSockets → Hindi voice check-ins (Sarvam STT/TTS) with pgvector patient memory.

serverpod
dart
flutter
postgres + pgvector
gemini
sarvam
websockets

roles on one typed client: 3
AI smoke gates (extract + STT) green: 5/5
prescriptions activated without caregiver review: 0

what I learnedServerpod's auth, Future Calls, streams and pgvector map almost 1:1 to a care loop. The rule that kept the demo alive: mutate Postgres, then post a CareUpdate. AI drafts; humans confirm.

repo ->
hackathon build: shipping

§02 / notebook

Lab log

smaller experiments, hackathons and write-ups. including the ones that didn't work.

Fig. 10 / notebook.log[SCROLL · 16]

Lab log of experiments and builds, newest first. About ten rows visible; scroll for the rest.
date	entry	tag	result
2026-10	Dhātrī, Serverpod care companion	HACK	BE + AI final-ready: Future Calls, CareUpdate stream, Sarvam Hindi voice, pgvector memory. Flutter wiring next.
2026-10	open-aiops governance (Intervues Club)	OSS	router, failover, retry with full jitter. public PRs merged into SupriyankaKharade/open-aiops.
2026-10	w2v-BERT 2.0 vs Whisper-large encoders	ML	OOF 0.9346, best so far. public LB didn't move; the changes landed in the private split.
2026-09	Fine-tuning Whisper-large on 826 clips	ML	lost to a frozen probe, 0.904 vs 0.925. overfits by epoch 5.
2026-09	Face-tracking water gun	IOT	MediaPipe face centre to pan/tilt angles to two ESP32 servos. shoot is still a stub.
2026-07	AssetFlow, 8-hour hackathon	HACK	owned the asset track: registry, allocation state machine, transfers, overdue flagger.
2026-05	Gemini 3.1 Pro vs 3 Flash, zero-shot grading	EVAL	Flash wins File Structures by 0.42 MAE, loses Math by 0.54. no free lunch.
2026-05	Sarvam document OCR as a grading front-end	EVAL	benchmarked, then removed. documented why so nobody re-tries it blind.
2026-04	Kannada name generator, char-level GRU	ML	loss 2.08 to 1.41 over 10k epochs. some names are real, most are merely plausible.
2025-12	OWASP Top 10 lab series	SEC	17/17 PortSwigger labs written up. the README updates itself via Actions.
2025-11	DFA-based intrusion detection, from scratch	SEC	Snort-style rules compiled to a DFA plus PCRE, TCP reassembly, live dashboard.
2025-11	Zero-knowledge medical records vault	SEC	AES-256-GCM with RSA-OAEP key wrap in the browser. the server only ever sees ciphertext.
2025-09	Samudra Prahari, Smart India Hackathon	HACK	offline BLE-mesh hazard reports with image verification before broadcast.
2025-08	REINFOSEC CTF sprint	SEC	100+ challenges in 8 weeks. TryHackMe top 2%.
2025-05	Green Quest, Social Hackathon	HACK	adopt a plant on a map, AI-verified care quests, and a plant you can chat with.
2024-10	Green Terrace, BuzzOnEarth @ IIT Kanpur	HACK	3rd place. first podium, first all-nighter that paid off.

§03 / about

About

the readable version of the resume.

about.md · utf-8 · lf[README]

I'm Nishanth. I build LLM systems that have to be right, then the boring code that keeps them right.

Final-year computer science at Bangalore Institute of Technology (IoT, cybersecurity and blockchain track, CGPA 8.50). AI/ML intern at Kwilo AI since April 2026, with a two-month break in the middle, working on answer-script grading and the product around it.

## how I work

I don't pick a lane. I pick a problem and learn whatever lane it lives in. In two years that has meant:

100+ CTFs to learn how web apps break (REINFOSEC, 2025)
Spring Boot to learn how they're built (Goloka IT, 2025–26)
LangGraph, to make an agent investigate instead of autocomplete
vision LLMs and an eval harness, to grade handwriting against teachers
Soroban, to store a notepad on the Stellar chain, mostly to see if I could
servo trigonometry, to aim a water gun at faces (it misses less now)
Postgres row-level security, because one school's fees are not another school's business

## currently

shipping ERP and AI features at Kwilo
open-source contributor on open-aiops (Intervues Club): LLM provider routing, failover, retries
building Dhātrī for a Serverpod hackathon: Hindi voice care companion for elders
290 LeetCode problems in, 253 of them in Java

## off the clock

Lazy Monks: four of us, 10+ hackathons, one podium (3rd at BuzzOnEarth, IIT Kanpur). First place at Turing Much, the college ML competition. Core member of cryptX, the college technical team.

Fig. A / subject[NO PHOTO]

photo: N/A. which are also my initials.

Fig. B / facts[VERIFIED]

based in: Bengaluru
graduating: Jul 2027
CGPA: 8.50 / 10
LeetCode: 290
TryHackMe: top 2%
certs: OCI GenAI Pro, APIsec ACP

§04 / skills

Skills, honestly

no logo wall. you can't grep a logo.

Fig. 11 / skills.txt[SELF-REPORTED]

Skills by domain and how often I use them
domain	dailyused this week	comfortableshipped it, no docs for basics	dabblingshipped once, docs open
LLMs & agents	few-shot prompt design Gemini on Vertex AI Azure OpenAI eval harnesses Sarvam STT/TTS	LangGraph LangChain RAG with pgvector AWS Bedrock multi-agent loops provider failover / retry	Claude API
Models & data	pandas NumPy	PyTorch scikit-learn Whisper / w2v-BERT probing matplotlib	TensorFlow OpenCV MediaPipe federated learning
Backend	FastAPI SQLAlchemy async PostgreSQL Alembic pytest Serverpod	Flask Spring Boot REST WebSockets Postgres RLS	Supabase Firebase Prisma
Frontend	React TanStack Query Flutter	Tailwind Vite React Native (Expo) vitest	Next.js Phaser
MLOps & cloud	Git Docker CI on GitHub Actions	GCP Cloud Run + GCS Kaggle GPU pipelines uv	Azure Container Apps AWS EC2 / S3 Cloudflare
Languages	Python TypeScript Dart	Java SQL Bash	C C++ Rust (Soroban)
Security	none right now (was daily in 2025)	Burp Suite nmap OWASP Top 10 API security gobuster	sqlmap hydra hashcat Scapy Web Crypto

Fig. 12 / contact[OPEN]

Got a problem that has to work?

email: nishanthantony5@gmail.com
phone: +91 96321 48834
github: github.com/Nish344
linkedin: in/nishanth-antony
leetcode: u/Nish345
resume: resume.pdf

$ open mailto:nishanthantony5@gmail.com