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$
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I will build and deploy ai agents with langchain langgraph on cloud
Mohamed Gouda
Level 2
About this gig
Need an AI agent that actually works in production? Not just a demo.
As a Senior DevOps/MLOps Engineer with 9 certifications (GCP DevOps Engineer, CKA, AWS Developer, Terraform Associate), I build AND deploy AI agents end-to-end from LangChain code to production cloud.
What I deliver: AI agents with LangChain/LangGraph tool use, memory, reasoning Multi-agent orchestration with LangGraph workflows RAG integration with vector databases (Pinecone, ChromaDB, pgvector) API endpoints via FastAPI ready for frontend integration Docker containerization + cloud deployment (AWS, GCP, Azure) CI/CD pipeline auto build, test, deploy on every push LLMOps monitoring latency, tokens, cost, errors Infrastructure-as-Code with Terraform
Why me? Most AI devs build agents but can't deploy them. Most DevOps engineers can deploy but can't build agents. I do both managing 14+ cloud AI environments daily.
Perfect for: Businesses wanting custom AI agents Startups building AI-powered products Teams replacing manual workflows with agents
Message me to discuss your use case.
Get to know Mohamed Gouda
Mohamed Gouda
Streamlining DevOps with expertise, innovation, and efficiency!
Level 2
- FromEgypt
- Member sinceOct 2023
- Avg. response time1 hour
- Last delivery2 months
Languages
English, Arabic, German
👋 I'm Mohamed — Senior DevOps/MLOps/LLMOps Engineer with 9 certifications (GCP DevOps, CKA, AWS Developer, Terraform) managing 14+ cloud AI/ML environments daily.
I take AI apps from localhost to production — containerized, automated, monitored, and scalable on AWS, GCP, and Azure.
→ Deploy AI/LLM apps (LangChain, RAG, FastAPI)
→ Docker, Kubernetes, Terraform — always IaC
→ CI/CD pipelines for AI workflows
→ LLMOps monitoring & observability
→ Build + deploy AI agents end-to-end
Most devs build but can't deploy. I do both. Message me first!
My Portfolio
FAQ
What kinds of AI agents can you build?
Research agents, customer support agents, document Q&A, data analysis agents, workflow automation, code review agents, email assistants — any task that benefits from LLM reasoning + tool use.
What's the difference between a simple agent and multi-agent?
A simple agent uses 1-2 tools for a single task. Multi-agent systems use LangGraph to orchestrate multiple specialized agents working together — like a research agent feeding results to a writing agent.
Which LLMs do you support?
OpenAI (GPT-4o), Anthropic (Claude), open-source (Llama, Mistral, DeepSeek). I design agents to be model-agnostic so you can swap providers without code changes.
Do you handle both building and deployment?
Yes — that's what makes this gig unique. You get working agent code AND production infrastructure. No handoff needed between an AI dev and a DevOps engineer.
Can the agent connect to my existing tools?
Yes. I build custom tools for APIs, databases, file systems, web scraping, email, Slack, and any service with an API. The agent uses these tools autonomously.
What about cost optimization?
I implement caching, token optimization, model routing (expensive model for hard tasks, cheap model for simple ones), and monitoring dashboards so you always know what you're spending.
Will I get documentation?
Standard and Premium include full docs — architecture diagrams, API reference, agent behavior specs, deployment runbooks, and cost projections.
Can you add features later?
Yes. The modular architecture makes it easy to add new tools, agents, or capabilities. I offer subscriptions for ongoing development and maintenance.
