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I will architect agentic rag and langgraph multiagent ai systems
Muhammad Hamza
Over deze dienst
Standard large language model integrations frequently fail in complex enterprise environments due to context loss and unchecked hallucinations. To guarantee accuracy and operational safety, businesses require deterministic, stateful multi-agent systems. As an AI Engineer specializing in Agentic RAG and Multi-Agent Orchestration, I utilize LangGraph to separate cognitive reasoning from execution. I build hierarchical and supervisor-led agent networks that can self-correct, utilize custom tools, and deliver production-safe outputs.
Core Engineering Deliverables:
- LangGraph Topologies: Design of complex, stateful multi-agent architectures (supervisor and hierarchical networks).
- Hybrid Search Optimization: Implementation of hybrid semantic and BM25 search engines using Qdrant alongside Redis caching to drastically cut latency.
- Guardrails & Deployment: Deployment of strict intent validation guardrails and containerized solutions ready for production deployment on Google Cloud Platform (GCP) or AWS.
From sophisticated internal knowledge assistants to autonomous research teams, I engineer robust systems designed to drive measurable business value. Let us build your AI infrastructure proper
Maak kennis met Muhammad Hamza
Muhammad Hamza
Senior AI Engineer, Agentic Systems, LangGraph and MCP Architect
- Afkomstig uitPakistan
- Lid sindssep 2021
- Gem. reactietijd1 uur
- Laatste levering1 jaar
Talen
Engels, Urdu
I am a Senior AI Engineer specializing in production-grade Agentic RAG and multi-agent orchestration using LangGraph. Standard LLM integrations often fail in complex environments due to context loss. I solve this by building deterministic, stateful AI ecosystems that drive measurable business value. My technical stack includes custom MCP servers for secure tool calling, Qdrant for optimized vector retrieval, and scalable FastAPI backends. I do not simply build basic API wrappers. Let us architect your AI infrastructure properly from the foundational layer upward.
Mijn portfolio
Veelgestelde vragen
Why do you use LangGraph instead of standard LLM integrations?
Standard large language model integrations frequently fail in complex enterprise environments due to context loss and unchecked hallucinations. LangGraph is utilized to separate cognitive reasoning from execution to build deterministic, stateful multi-agent systems that can self-correct and deliver
What is included in the Basic Architectural Blueprint package?
It is priced at $150 with a 3-day delivery window. It includes a comprehensive 1-hour system design consultation. It results in the delivery of a graphical architecture blueprint and tech stack recommendation for a proposed multi-agent system.
What does the Standard Single Domain RAG System package cover?
It is priced at $600 with a 7-day delivery window. It involves engineering a robust RAG pipeline utilizing Qdrant. It features an intelligent routing agent, Redis memory integration, and basic tool-calling capabilities.
What are the deliverables for the Premium Enterprise Agent Team package?
It is priced at $1,500+ with a 14-day delivery window. It provides full multi-agent orchestration featuring a supervisor and domain specialists. It includes advanced RAG, custom tool creation, safety guardrails, and Docker deployment configurations.
