Waqas Raza is an AI-Native Full-Stack Engineer based in Lahore, Pakistan. He is Top Rated on Upwork with over $180,000 earned, 93% job success score, and 12+ years of experience building production AI agents, LLM systems, Web3 platforms, and full-stack applications for global clients.

What does Waqas Raza specialize in?

Waqas Raza specializes in AI agent development (OpenAI, LangChain, LangGraph), LLM integration with RAG and guardrails, Web3 and Solidity smart contracts, full-stack development (Next.js, Node.js, TypeScript, Flutter), payment systems (Stripe), and data pipelines.

What is Waqas Raza's Upwork rating and track record?

Waqas Raza is Top Rated on Upwork with a 93% job success score, over $180,000 in total earnings, 12+ years on the platform, and 15 consecutive five-star reviews. He has completed projects across AI, Web3, SaaS, payments, and data engineering for clients globally.

Can Waqas Raza build AI agents and LLM-powered applications?

Yes. Waqas Raza builds production-grade AI agents with tool use, RAG, strict guardrails, and predictable cost controls using OpenAI, LangChain, LangGraph, and FastAPI. He has shipped a deep research agent, a DocOps automation agent, speech analytics platforms, and AI-powered pipelines.

Does Waqas Raza do Web3 and Solidity development?

Yes. Waqas Raza develops Solidity smart contracts on Ethereum and Base, DeFi banking platforms, ERC-4337 smart account systems, token launch studios, and milestone escrow contracts. He works with Foundry, Hardhat, and Ethers.js.

What technologies does Waqas Raza use?

Waqas Raza's core stack includes Next.js, TypeScript, Node.js, React, Flutter, Python, Supabase, PostgreSQL, Redis, Stripe, OpenAI, LangChain, LangGraph, Solidity, Foundry, Hardhat, Docker, AWS, and GCP.

Is Waqas Raza available for freelance projects?

Yes. Waqas Raza is available for freelance projects at 30+ hours per week with a typical response time of 0–4 hours. He can be hired through his Upwork profile at upwork.com/freelancers/waqasraza.

Where is Waqas Raza based?

Waqas Raza is based in Lahore, Pakistan. He works remotely with clients worldwide across the US, EU, and other regions. He is fluent in English and has worked with teams at Delivery Hero (Berlin) and Hello HD (EU startup).

AvatarKit AI — Case Study

The Problem

Most AI avatar products start as photo-to-video demos. That is useful for experimentation, but it is not enough for a business system where a team needs to create avatars, prove consent, control who can edit them, attach approved business knowledge, and eventually publish them into customer-facing workflows.

The risk profile is different from a generic media generator. A business avatar represents a person or brand. The platform has to treat identity, consent, workspace ownership, and future provider boundaries as core product infrastructure, not as policy text added after launch.

AvatarKit AI needed a phase-driven foundation that could grow into talking avatars for websites, kiosks, onboarding, sales, support, education, and service workflows without shipping unsafe public runtime features too early.

What Was Built

AvatarKit AI is a Turborepo monorepo for business avatar infrastructure:

apps/web - Next.js dashboard and product UI
apps/api - TypeScript API service foundation
apps/widget - placeholder for the future embeddable runtime
packages/config, packages/types, packages/ui - shared configuration, contracts, and UI
services/ai-runtime, services/ingestion, services/media-worker - Python service foundations for future AI, ingestion, and media workloads
prisma - database schema for the product state model

The current implementation is complete through Phase 4: local auth, session cookies, workspaces, role-aware access control, onboarding, Avatar Studio drafts, source-photo upload and validation, private preview routes, consent records, identity-safety acceptance, and setup checklist state.

Consent-First Avatar Creation

The core product loop is intentionally gated:

Create avatar -> verify consent -> upload source photo -> configure behavior -> add knowledge -> test -> publish

Only the early phases are implemented today. That is a deliberate engineering decision. Public widget access, voice cloning, AI runtime calls, knowledge ingestion, avatar video generation, lead capture, billing, and realtime streaming are locked behind later milestones.

This keeps the system honest. A publishable business avatar should not exist until the platform can prove workspace ownership, source-photo validity, and consent state.

Workspace and Identity Boundaries

Every major business entity is workspace-scoped. Users create or switch active workspaces, and dashboard routes enforce workspace-aware protection. Avatar records, setup state, source photos, and consent records are not global objects that can be accidentally leaked between teams.

Source photos are validated server-side for JPG, PNG, and WEBP constraints, stored in private local storage during development, and exposed only through protected dashboard preview routes. Consent records are tied to the current valid source photo, so replacing the source image changes the consent state that matters.

Provider-Ready Architecture

The architecture separates product state from future avatar engines. TypeScript owns the dashboard, API, widget, SDK, permission boundaries, and shared contracts. Python owns the future AI orchestration, retrieval, ingestion, media, and GPU-oriented workloads.

That separation matters because commercial avatar providers, self-hosted engines, LLMs, TTS, STT, and media workers should sit behind internal service boundaries. The product should not be coupled to one provider's API shape or safety model.

Key Engineering Decisions

Phase guardrails over premature features. The repository explicitly documents what is implemented and what is not. Voice libraries, knowledge ingestion, public widgets, AI runtime calls, publishing, and billing are planned later rather than half-built into the current phase.

Workspace-scoped data model. Avatar infrastructure is multi-tenant from the start. Auth, roles, active workspace switching, route protection, and avatar CRUD all follow the same ownership boundary.

Consent as product state. Consent is not just copy on a form. It is recorded against the current valid source photo and reflected in setup checklist state, which makes it enforceable by later publish and runtime flows.