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Faizan Arshad
@mfaizanar
I Build AI Agents, RAG Pipelines, and Full Stack Apps That Work in Production
Pakistán
Inglés
Sobre mí
I am a full stack developer and AI engineer with four plus years shipping software to production, not just demos. Expertise covers LLM integration, AI agents, and RAG systems using LangChain, LangGraph, and Claude and OpenAI APIs, backed by React, Next.js, Node.js, and PostgreSQL.
My services include RAG chatbots on your own data, AI agents that call tools and take real actions, full stack apps with AI built in, and computer vision or data extraction pipelines.
I am available 24/7 and happy to set up a quick Zoom call early on so we can walk through your requirements and start drafting.
Habilidades
Faizan Arshad
desconectado •
Revisa mis servicios
Clases online de codificación
I will teach python programming and data science
Desarrollo de chatbots de IA
I will build production ready ai agents, rag systems, and llm saas apps
Porfolio
Experiencia laboral
Full Stack Developer & AI Engineer (AI Agents, RAG, LLM)
Upwork • Freelance
Feb 2022 - Present • 4 yrs 6 mos
● Designed, prototyped, and deployed production LLM agents and RAG pipelines using LangChain and LangGraph for clients across healthcare, finance, and technology, maintaining Top Rated status with a 100% Job Success Score. ● Built an agentic clinic scheduling assistant using the Claude API with real tool calling for live schedule reads and updates, applying ReAct style reasoning to decide when to query data, ask a clarifying question, or confirm an action. ● Designed scalable RAG document retrieval pipelines over private knowledge bases for enterprise clients, including a legal research system over 10,000 plus documents that cut research time by 70 percent. ● Conducted structured AI evaluation and benchmarking sessions for a client's AI product, assessing model behavior, reliability, and output quality across candidate LLMs. ● Built and deployed full stack applications with Node.js, React.js, and FastAPI, containerized with Docker and deployed to AWS and GCP.
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