PORTFOLIO / 2026BENGALURU · 12.97°N
AI / ML ENGINEER · AGENTS · LLMS
VATSA
JOSHI
LANGGRAPH ✦ PYTORCH ✦ GEMINI ✦ FASTAPI ✦ NEXT.JS ✦ RAG ✦ FINE-TUNING ✦ MULTI-AGENT ✦ YOLO ✦ FAISS ✦ DOCKER ✦ AWS ✦ LANGGRAPH ✦ PYTORCH ✦ GEMINI ✦ FASTAPI ✦ NEXT.JS ✦ RAG ✦ FINE-TUNING ✦ MULTI-AGENT ✦ YOLO ✦ FAISS ✦ DOCKER ✦ AWS ✦
CS Grad. I design, train & ship intelligent systems — fine-tuned models to multi-agent products in production. Now open to AI/ML roles.SCROLL
AVAILABLE FOR AI / ML ENGINEERING ROLES OPEN TO RELOCATE BENGALURU / REMOTE 2026 AVAILABLE FOR AI / ML ENGINEERING ROLES OPEN TO RELOCATE BENGALURU / REMOTE 2026
( 01 ) — WHAT I DO

I build agentic AI systems and fine-tune models that hold up under real production load — legal AI, multi-agent workflows, vision & low-resource NLP.

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PROJECTS SHIPPED
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BEST MODEL ACCURACY
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OCR LANGUAGES
( 02 ) — FOCUS AREAS

Three things I go deep on.

01

Agentic Systems

Multi-agent orchestration, tool use & conditional routing — drafting, profiling and automation that runs end-to-end.

LANGGRAPH · CREWAI · MCP
02

Model Fine-Tuning

LoRA / QLoRA & distributed training for low-resource translation and domain adaptation — cheaper inference, sharper output.

UNSLOTH · DEEPSPEED · HF
03

Vision & NLP

Real-time detection, multilingual OCR and semantic retrieval — pipelines that read, parse and index the messy real world.

YOLO · PADDLEOCR · FAISS
( 03 ) — HOW THE WORK GETS BUILT

From raw data to deployed system.

SCROLL TO RUN THE PIPELINE ↓

PIPELINE / 00%
INGEST
01

Ingest & Ground

Parse corpora, OCR & structure into DB/CSV-grounded sources.

02

Train & Fine-Tune

LoRA / QLoRA adaptation, distributed runs & eval loops.

03

Orchestrate

Wire agents, tools & routing into a reliable workflow.

04

Ship & Harden

Deploy on FastAPI / Docker / AWS — measure, cut cost, repeat.

( 04 ) — MOST RECENT WORK

BharatLaw.ai

@ PROMACT · JAN–MAY 2026
LEGAL AI · NOW IN PRODUCTION

An AI-powered legal-technology product for Indian law firms. I cut NER extraction cost 25% on an 80-lakh judgment corpus by replacing a 7-stage LLM pipeline with a 2-call Gemini-2.5-Flash-Lite architecture — and hit 98% document-level zero-failure across 40-lakh test rows.

MULTI-AGENT DRAFTING
JUDGE PROFILING
WINDOWED PRECEDENT EXTRACTION
DB/CSV-GROUNDED STRUCTURE
TRANSLATION · INDEXING · RETRIEVAL
VIEW ALL PROJECTS →
( 05 ) — SELECTED WORK

A few favourites.

ALL PROJECTS →
01 / AGENTS

AutoSteer

Multi-agent orchestration routing each request to the right specialist — 42 agents, config-driven.

PYTHON · MULTI-AGENT · YAML
02 / RAG

ChemRAG

Compliance agent with hybrid RAG, grounding guardrails and LLM-as-judge evaluation.

RAG · LITELLM · LANGFUSE
03 / TOOLING

claude-multimodel

Swap LLM providers inside Claude Code — a published npm package, global install.

TYPESCRIPT · NPM · CLI
( 06 ) — LET'S BUILD
Get in touch ↗