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m_danyal_

Muhammad Danyal

@m_danyal_

AI Engineer specializing in RAG, AI Agents, Voice AI and LLMs

Pakistán
Inglés, Urdu
Parte de la información aparece en idioma inglés.
Sobre mí
I am an AI Engineer specializing in RAG systems, LangGraph AI agents, Voice AI, LLM fine-tuning, and production-ready AI backends. I build AI assistants for documents and knowledge bases, agents that use tools and APIs, voice agents for calls and booking, and custom LLM solutions using LoRA and QLoRA. My stack includes Python, FastAPI, LangGraph, LangChain, vector databases, REST APIs, and Docker. I focus on clean code, reliable integrations, testing, deployment, and clear documentation.... Lee más

Habilidades

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m_danyal_
Muhammad Danyal
desconectado • 
Tiempo medio de respuesta: 1 hora

Revisa mis servicios

Perfeccionamiento del modelo de IA
I will fine tune llms with lora and qlora
DeUSD 35 / proyectoMás información
Integraciones de IA
I will build ai voice agents and ai receptionists
DeUSD 90 / proyectoMás información

Porfolio

Experiencia laboral

Self_Employed

AI and Generative AI Engineer

Self Employed • Trabajador autónomo

Nov 2025 - Present • 11 mos

Built and maintained production-ready AI systems for real-world business use cases, including RAG chatbots, LangGraph and LangChain AI agents, voice AI assistants, LLM applications, and model fine-tuning workflows. Developed RAG systems for PDFs, documents, websites, and business knowledge bases using vector databases, embeddings, semantic search, source citations, and retrieval optimization. Built stateful and multi-step AI agents with tool calling, API integrations, memory, workflow orchestration, MCP tools, human-in-the-loop approval, and multi-agent architectures. Developed voice AI systems for inbound and outbound calls, AI receptionists, appointment booking, lead qualification, CRM and calendar integrations, call transfer, and conversation summaries. Worked on LLM fine-tuning using LoRA and QLoRA, including dataset preparation, training, evaluation, inference, and model integration. Built Python and FastAPI backends, REST APIs, database integrations, Docker-based deployments, and production AI pipelines. Focused on clean architecture, reliable integrations, testing, maintainable source code, clear documentation, and deployment-ready solutions.