Freelance AI Developer for Production-Ready AI Products

Q: Can you build the backend as well?

Yes. I handle the frontend, backend APIs, database, authentication, AI integration, deployment, and supporting infrastructure as part of the same project.

I build AI applications, RAG systems, AI agents, and intelligent product features for startups and businesses - from architecture and model integration to deployment.

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Production web, mobile, and AI systems · Available remotely worldwide

10,000+ Students Served*

450+ Faculty Users*

9 Departments*

Web + Mobile + AI Systems

Payments + Subscriptions

Offline-First Architecture

Problems I Solve

Instead of treating AI as an isolated experiment, I integrate AI into complete software products with real workflows, databases, APIs, and production reliability.

AI Product Development

Build AI-powered products that go beyond a basic chatbot, with model integration, structured workflows, tool use, and production APIs.

RAG Applications

Turn private documents and knowledge bases into searchable, source-aware AI experiences with accurate passage retrieval and citation.

AI Agents

Build multi-step AI workflows with specialized agents, structured outputs (Pydantic), tool calling, evaluation, and LangGraph orchestration.

AI Feature Integration

Add practical AI capabilities - answer grading, automated summarization, smart search - into an existing SaaS, web application, or mobile app.

AI MVP Development

Turn an AI product idea into a working MVP with the architecture, database schema, and security needed to evolve beyond a demo.

AI Automation

Replace repetitive manual processes with AI-assisted background pipelines, FastAPI webhooks, and structured data extraction.

Selected Production AI Work

Real projects demonstrating end-to-end AI engineering - from model orchestration to production deployment.

Hero Case Study · AI Preparation Platform

Thozhan AI - Built an AI-powered preparation operating system across mobile & web

Live Site Play Store

Thozhan AI combines personalized study planning, retention modeling, AI-assisted answer evaluation, mistake intelligence, progress analytics, and subscription billing in a live mobile and web platform. Learn more on our Mobile App Development page.

The Problem

Competitive exam candidates needed objective evaluation for handwritten responses, retention modeling, and personalized study schedules that stay functional offline.

Engineering Highlights

React Native (Expo) mobile + Next.js web
Gemini-powered handwritten answer grading
pgvector RAG for contextual questions
SM-2 spaced repetition & ELO readiness scoring
Offline-first SQLite caching & sync
Razorpay, Apple IAP & Google Play Billing
Sentry monitoring & Supabase backend

Outcome & Scale

Live product on Google Play and web. Deployed with production subscription billing across 3 payment providers.

Production Status: Deployed & Monetized

AI Research Project

MedAgent - Designed a multi-agent AI diagnostic reasoning workflow

Live Demo Source Code

A LangGraph-based system that coordinates specialized agents for structured medical reasoning, evidence gathering, causal-chain construction, follow-up questioning, and consensus.

The Problem

Single-prompt LLMs struggle with multi-step medical diagnosis and evidence verification. Needed a structured multi-agent architecture with evidence bounds.

Engineering Highlights

5-agent LangGraph orchestration pipeline
IBIS structured argumentation framework
Hierarchical Disease Causal Chain (HDCC)
Modular 5-provider LLM factory (OpenAI, Gemini, Anthropic, Azure, Ollama)
Dynamic patient interaction agent
Pydantic structured output validation

Project Result

Demonstrates multi-agent coordination with fallbacks across model providers.

Project-Reported Metric: HDCC chain validity 98% → 100%

RAG Application

PaperBrain - Built a source-aware document RAG application

Live Demo Source Code

Users upload complex documents, the system chunks and embeds them into a vector store, retrieves relevant passages, and generates source-attributed answers using a RAG pipeline.

The Problem

General LLMs hallucinate when asked about internal documents. Needed strict passage retrieval with exact source citations.

Engineering Highlights

Document parsing & semantic chunking
FAISS vector index & sentence-transformers
LangChain QA retrieval pipeline
Gemini Pro synthesis with citation enforcement
Conversational memory management

Project Result

Open-source tool for document search and citation-backed Q&A.

Self-Reported Benchmark: ~21 ms average retrieval latency

Also Shipped in AI

Agent Sterling

Gemini-powered social media automation platform with FastAPI backend and background task queues.

NeuraCare

React Native / Expo mobile app combining Google Generative AI with federated learning principles. See Mobile Apps.

MLB Storyteller

Gemini + MLB Stats API automated sports storytelling platform deployed on GCP with FastAPI. See Full-Stack.

Why Work Directly With Me?

One Engineer Across the Stack

You don't need to coordinate a frontend developer, backend developer, and AI engineer. I take ownership across UI, APIs, vector stores, and deployment.

AI + Conventional Engineering

AI features still need authentication, databases, REST APIs, billing, caching, and infrastructure. I build both sides seamlessly.

Product-Minded Implementation

I focus on the real user workflow and business objective - building features that solve problems rather than tech demos.

Production Concerns Built In

Payments, security, offline caching, rate limiting, error handling, and monitoring (Sentry) are included in the core build, not added as afterthoughts.

How the Engagement Works

A clear, transparent process from initial requirement to shipped product.

Define the Problem

We identify what you're building, the target users, the core workflow, and what needs to exist for the first useful release.

Scope the Build

I break the project into clear deliverables, technical requirements, vector/model integrations, and milestone timelines.

Build & Test

Development proceeds iteratively across frontend, backend APIs, database schema, AI pipelines, and third-party services.

Deploy & Ship

I handle production deployment, store submissions, environment setup, domain configuration, and final handoff.

Ongoing Support

Continued improvements, new feature additions, monitoring, and technical maintenance available on a retained basis.

Project Fit

Good Fit If You Need

An AI-powered product or MVP built from scratch.
RAG search or AI agents integrated into an existing product.
A developer who can own frontend, backend, AI, and deployment.
Handwritten or document evaluation pipelines.
Production features: payments, subscriptions, auth, caching.

Probably Not a Fit If

You want a static template page with no custom logic.
You need a large 10-person agency team with non-technical managers.
You are looking for pure design/mockup work with no coding.
You want prompt engineering tips without building actual software.

Technologies I Work With

AI & ML Frameworks

LangGraph LangChain RAG Pipelines Gemini API OpenAI API Pydantic Transformers

Backend & APIs

Python FastAPI Node.js Express REST APIs Prisma

Data & Vector Stores

PostgreSQL pgvector FAISS Supabase SQLite Firebase

Frontend & Mobile

React Next.js React Native Expo TypeScript

Infrastructure & Payments

Docker Vercel GCP Razorpay Apple IAP Google Play Billing

Frequently Asked Questions

Do you build AI agents or only chatbots?

I build both. The work ranges from embedded AI features and RAG search to multi-step agent workflows using LangGraph with tool calling, structured outputs (Pydantic), evaluation, and backend orchestration.

Can you integrate AI into an existing product?

Yes. AI can be added to an existing web or mobile application through REST APIs, backend microservices, RAG pipelines, agent workflows, or model-powered product features without requiring a full rewrite.

Can you build the backend and frontend as well?

Yes. I handle the frontend, backend APIs, database schema, authentication, AI integration, deployment, and supporting infrastructure as part of the same project - giving you a complete working product.

Do you work on MVPs?

Yes. The goal is to build the smallest useful version of the product while keeping the architecture practical for future development and scale.

Do you work with international clients?

Yes. I work remotely with startups and founders across the US, UK, Canada, Australia, Singapore, UAE, and Europe. Communication is typically async with overlap hours as needed.

How does pricing work?

I offer fixed-scope project pricing for clear requirements, MVP packages for new products, and ongoing engineering support on a retained basis. Get in touch for a quote tailored to your project.

Let's build the thing you're trying to ship.

Tell me what you're building, where you are today, and what you need help with. I'll review the requirements and determine the most practical way to move forward.

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