Arjun Thakur is a Principal/Lead AI Engineer, Architect, and Fractional CTO based in Indore, India. He builds production agentic AI systems end-to-end — LangGraph agent graphs, RAG pipelines, WebRTC voice agents — with 10+ years of engineering experience at Amazon, Agoda, and high-growth startups. He is the founder of Yuvan, a voice-first AI math tutor currently live with 5 paying CBSE schools.

Yuvan is a voice-first AI math tutor for CBSE Class 10 students built by Arjun Thakur. It uses a LangGraph state machine, RAG over NCERT content on pgvector, OpenAI Realtime API for sub-1.5s voice latency, a math verifier safety net, and a semantic FAQ cache. It operates on a B2B2C model and has 5 paying CBSE schools as design partners.

What AI technologies does Arjun Thakur specialise in?

Arjun specialises in: LangGraph stateful agent graphs, LangChain, RAG with pgvector and Supabase, OpenAI GPT-4o/4o-mini and Realtime API, WebRTC voice agents, LangSmith evals and observability, multi-agent orchestration, prompt engineering, guardrails and verifier patterns, FastAPI async Python backends, and Next.js 15 for AI-native frontends.

Is Arjun Thakur available for hire?

Yes. Arjun is available for Lead/Principal AI Engineer roles, AI Architect positions, Fractional CTO engagements, and senior freelance AI consulting globally. He is remote-first but open to short-term relocation (2-6 weeks) for onboarding. Based in Indore, India.

Where has Arjun Thakur worked before?

Arjun has worked at Amazon (SDE2, Payments), Agoda.com (Senior Software Developer, Scala/Cassandra/Kafka), PhysicsWallah (Director of Full-Stack Engineering, led 15 engineers), ForaySoft/Wayfair (Senior Engineering Manager), Nolan EduTech (VP Engineering, $3.6M revenue in one year), and CoinSwitch Kuber (Lead Developer, scaled from 600K to 10M users).

Founder @ Yuvan — AI Math Tutor ex-Amazon · ex-Agoda AWS Certified Solutions Architect

I Build Production Agentic AI Systems End-to-End

Principal AI Engineer · Architect · Fractional CTO

10+

Years Production Engineering

Paying Schools on Yuvan

900+

AI Prototype Users

LangGraph agent graphs · RAG pipelines · WebRTC voice agents · multi-tenant AI backends. Engineering discipline, not vibes-based prompting — LangSmith evals and retrieval-precision metrics before anything ships to production.

Book Free Consultation See What I Build

Current Availability

Open To

Lead / Principal AI Engineer — agentic systems, RAG, voice AI
AI Architect — end-to-end system design for AI-native products
Fractional CTO — embed AI features inside your product stack
Remote-first (global) — short-term relocation (2–6 weeks) for onboarding if needed
Based in: Indore, India
Training and Speaking: LangGraph, RAG, voice agents, agentic AI
Freelance / Contract: senior AI engineering embedded in your team

Not Open To

Permanent relocation outside of Indore

Services: What I Build and How I Engage

From greenfield agentic systems to embedding AI inside an existing stack

Agentic AI Systems

LangGraph stateful agent graphs and multi-agent orchestration
Tool/function calling, guardrails, verifier patterns
LangSmith evals, agent traces and observability
Prompt engineering and structured output design

RAG and Vector Search

End-to-end RAG pipeline design and build
pgvector / Supabase ingestion, chunking, retrieval evals
Hybrid search and semantic FAQ caching
Retrieval precision benchmarking

Voice AI Pipelines

OpenAI Realtime API + WebRTC (STT, TTS, VAD, barge-in)
Sub-1.5s p95 first-audio latency engineering
Server-side reasoning and ephemeral token architecture
Latency / cost benchmarking across voice stacks

Fractional CTO / AI Architect

Technical strategy and AI product roadmap
Team building, hiring, and AI tooling rollout
Architecture review and cost engineering
Founder-mode: own product and GTM alongside code

AI Integration (Existing Stack)

Embed agentic features inside Java / Python / Node backends
AI-native FastAPI services alongside existing systems
LLM cost engineering: caching, model selection, batching
Multi-tenant AI: auth, RLS, data isolation

AI Training and Workshops

LangGraph, RAG, voice agents — for engineering teams
Custom curriculum from practitioner experience
System design, backend, distributed systems
AI-assisted development culture and tooling

Not sure which engagement fits?

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Flagship Project: Yuvan — Voice-First AI Math Tutor

A production agentic system designed, built, and shipped solo — end-to-end

Yuvan is a live, paying-customer AI product for CBSE Class 10 students. 5 schools are paying design partners on a B2B2C model (school recommends, parent pays). Full agentic architecture built by Arjun:

LangGraph State Machine

Per-doubt agent: embed → semantic FAQ-cache lookup → RAG retrieval over NCERT chunks → GPT-4o-mini generation → math verifier → topic auto-tagger → persistence → re-explain loop on detected student confusion.

WebRTC Voice Pipeline

OpenAI Realtime (STT + TTS + VAD + barge-in). Backend-minted ephemeral tokens. Sub-1.5s p95 first-audio latency. All reasoning, RAG, and verification stays server-side.

RAG on pgvector

Ingestion, chunking, OpenAI embeddings, top-k retrieval, citation-grounded answers. Global semantic FAQ cache (cosine >= 0.92) targeting ~30% hit rate to push per-session OpenAI cost below the unit-economics ceiling.

Multi-Tenant Backend

4 user roles: Student, Parent, School Admin, Super Admin. Supabase Auth + Postgres RLS for tenant isolation. Parental-consent flow (DPDP / NCPCR-aligned). Weekly parent reports and a school engagement dashboard.

Math Verifier Safety Net

Re-checks every numerical claim before the AI speaks it (under 1s overhead). The difference between a demo and a tutor that schools actually trust.

LangSmith Observability

Full agent traces, retrieval-precision metrics, and evals before any change ships to production. Engineering discipline, not vibes-based prompting.

Stack: LangGraph · FastAPI (async) · OpenAI GPT-4o-mini · OpenAI Realtime API · pgvector · Supabase · Next.js 15 · Railway · Vercel · LangSmith

More Project Highlights and Case Studies

AI R&D: Multi-Agent Benchmarking

What: Benchmarked LangChain router, supervisor, and plan-and-execute patterns on real student conversations.

Outcome: Selected architecture Yuvan ships on. Shipped early prototype to 900+ users — validated demand before building the full product.

Stack: LangChain · OpenAI · Python

Voice Agent Stack Shootout

What: Prototyped 4 voice stacks — OpenAI Realtime, Whisper + ElevenLabs, Whisper + Coqui, Gemini Live — with latency/cost benchmarks.

Outcome: Data drove the Realtime + WebRTC selection for Yuvan.

Stack: OpenAI Realtime · WebRTC · Python

Amazon Payment Security

Challenge: Protect payment systems from CSRF misuse globally.

Solution: Throttling system on DynamoDB + Java.

Result: 19.7% lower security impact; 15.3% reduction in coupon misuse in first month.

CoinSwitch: 600K to 10M Users

Challenge: Monolith could not handle crypto bull-run traffic.

Solution: Migrated to Python/FastAPI microservices; Redis caching (30% latency drop); PgBouncer connection pooling (40% further latency drop).

Result: System held stable through a 16x user surge.

Agoda A/B Testing Platform

Challenge: High-scale experimentation infrastructure for product managers.

Solution: In-house pipeline on Scala + Cassandra + Kafka processing millions of messages.

Result: 22% faster time-to-insight.

Wayfair Promotions Migration

Challenge: Zero-downtime microservice migration for seller promotions.

Solution: Java, Spring Security, PostgreSQL, Docker, Kubernetes on AWS.

Result: 28% fewer system failures; 25% more transaction volume handled.

Core Technical Skills

Agentic AI and LLMs: LangChain, LangGraph (stateful agent graphs), RAG, semantic FAQ caching, function/tool calling, prompt engineering, evals, agent observability (LangSmith), guardrails and verifier patterns, multi-agent orchestration

LLM Providers and Voice: OpenAI GPT-4o / 4o-mini, Realtime API, Embeddings (text-embedding-3), Anthropic Claude, Gemini, OpenAI Whisper, WebRTC voice agents, streaming + interruption handling

Vector and Retrieval: pgvector, embeddings (OpenAI, Cohere, BGE), hybrid search, chunking strategies, cosine-similarity caching, retrieval evals

AI-Native Backend: Python 3.11+, FastAPI (async), Pydantic v2, asyncpg, pytest-asyncio

Other Backend: Java 21, Spring Boot, Spring Security, Hibernate JPA, Scala

Architecture: Microservices, distributed systems, event-driven (Kafka), HLD/LLD, design patterns, SOLID, clean code, REST + OpenAPI, TDD

Data: PostgreSQL, pgvector, Cassandra, DynamoDB, Redis, MongoDB, PgBouncer, Supabase (Auth + RLS)

Cloud and DevOps: AWS Certified Solutions Architect — S3, ECS, Lambda, IAM | Docker, Kubernetes, GitHub Actions, Jenkins, Railway, Vercel, Grafana, ELK

Frontend: Next.js 15 (App Router), TypeScript, React, Tailwind, shadcn/ui, KaTeX

Testing and QA: TDD, JUnit, Mockito, pytest, pytest-asyncio, httpx

AI-Assisted Dev: Claude Code, Cursor, GitHub Copilot, v0.dev

Leadership: Strategic planning, hiring and team growth (15+ engineers, INR 30Cr P&L), mentoring, curriculum design, stakeholder management, agile/scrum, founder-mode product + GTM

Frequently Asked Questions

What kind of AI systems does Arjun Thakur build?

Arjun builds production agentic AI systems from the ground up: LangGraph stateful agent graphs, RAG pipelines on pgvector, WebRTC voice agents using OpenAI Realtime API, multi-tenant AI backends with Supabase, and full-stack AI products with Next.js 15. He runs LangSmith evals and retrieval-precision metrics before anything is called production-ready.

Is Arjun available as a Fractional CTO or for freelance AI projects?

Yes. Arjun is available for fractional CTO engagements, senior freelance AI engineering contracts, and full-time Lead/Principal AI Engineer roles globally. Remote-first. Contact via the form or email at thakurarjun247@gmail.com.

What is Arjun Thakur's background before AI?

10+ years of production engineering: SDE2 at Amazon (payments/security), Senior Software Developer at Agoda (distributed systems with Scala/Cassandra/Kafka), Lead Developer at CoinSwitch Kuber (scaled 600K to 10M users), VP Engineering at Nolan EduTech (INR 30 crore revenue in one year), Senior Engineering Manager at ForaySoft/Wayfair, and Director of Engineering at PhysicsWallah (led 15-engineer team). Pivoted full-time into AI in late 2023, founded Yuvan in 2025.

How does Arjun approach production AI quality?

Arjun runs LangSmith evals, retrieval-precision metrics, and full agent traces before calling anything production-ready. He implements math verifier patterns to validate numerical claims, guardrails to prevent hallucinations, and semantic FAQ caches to manage per-session LLM costs. The same engineering rigor he brought from Amazon and Agoda applies to AI systems.