You will get your GenAI & Agentic AI app deployed with Docker & K8s
Project details
You will get your GenAI or Agentic AI application containerized with Docker and deployed on Kubernetes with CI/CD — production-ready, scalable, and fully automated.
I deploy the AI apps most DevOps engineers can't handle: LLM agents, multi-agent orchestration (LangGraph, CrewAI, AutoGen), RAG pipelines, tool-calling agents, and GPU model serving.
What's included:
— Optimized Docker images for GenAI apps (Python, CUDA, PyTorch)
— Kubernetes deployment on EKS, GKE, or AKS
— GPU node pools and resource scheduling
— Agent orchestration deployment (LangGraph, CrewAI, AutoGen)
— CI/CD pipeline for automated builds and deploys
— Model serving with FastAPI, vLLM, or TGI
— Auto-scaling based on request load or GPU utilization
— Monitoring with Prometheus & Grafana
— Secrets management for API keys (OpenAI, Anthropic, HuggingFace)
— Full documentation and live walkthrough call
I'm a CKA-certified DevOps Engineer with AI developer training (LangChain, LangGraph, LLMOps). I bridge the gap between building AI agents and running them in production.
I deploy the AI apps most DevOps engineers can't handle: LLM agents, multi-agent orchestration (LangGraph, CrewAI, AutoGen), RAG pipelines, tool-calling agents, and GPU model serving.
What's included:
— Optimized Docker images for GenAI apps (Python, CUDA, PyTorch)
— Kubernetes deployment on EKS, GKE, or AKS
— GPU node pools and resource scheduling
— Agent orchestration deployment (LangGraph, CrewAI, AutoGen)
— CI/CD pipeline for automated builds and deploys
— Model serving with FastAPI, vLLM, or TGI
— Auto-scaling based on request load or GPU utilization
— Monitoring with Prometheus & Grafana
— Secrets management for API keys (OpenAI, Anthropic, HuggingFace)
— Full documentation and live walkthrough call
I'm a CKA-certified DevOps Engineer with AI developer training (LangChain, LangGraph, LLMOps). I bridge the gap between building AI agents and running them in production.
AI Algorithms
Generative Adversarial Network, Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI-Generated Code, AIOps, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, Gradio, Hugging Face, PyTorch, Streamlit, TensorFlowAI Models
ChatGPT, GPT-3, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$80
|
Standard
$220
|
Advanced
$450
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 14 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Batch Normalization | - | ||
Database Integration | - | ||
Detailed Code Comments | |||
Image Upscaling | - | - | - |
MLOps | - | ||
Model Deployment | |||
Model Documentation | - | - | - |
Model Monitoring | - | - | |
Model Testing & Optimization | - | - | |
Model Tuning | - | - | - |
Natural Language Processing | - | - | - |
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | - | - | |
Setup File | |||
Source Code |
Optional add-ons
You can add these on the next page.
Additional Revision
+$25Frequently asked questions
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DW
Daniel W.
Mar 21, 2026
You will get DevOps & DevSecOps Training: AWS, Docker, Kubernetes, IaC & CI/CD
Mohamed helped me with some tutoring for the CKA exam. We discussed different topics and practiced some labs together using https://killercoda.com too.
About Mohamed
Senior DevOps/AI Engineer
Banha, Egypt - 4:43 pm local time
My main work is managing production infrastructure across GCP, Azure, and AWS. I run 14+ multi-cloud environments supporting AI/ML teams across four countries — so my days are filled with Terraform, CI/CD pipelines, Kubernetes, monitoring, and keeping containerized workloads running smoothly. I've picked up 9 certifications along the way, including GCP Professional DevOps Engineer, CKA, AWS Developer Associate, and HashiCorp Terraform Associate.
I also build on the AI side — RAG chatbots, document Q&A systems, AI agents with tool use, and human-in-the-loop workflows, using LangChain, LangGraph, vector databases, OpenAI/Gemini APIs, and Google ADK. What I've found is that most AI developers need someone else to deploy and maintain what they build. I don't. I can take an AI system from idea to production — architecture, pipeline, containerization, CI/CD, deployment, monitoring — all under one roof.
Outside of infrastructure and AI, I build full-stack apps with React, TypeScript, Next.js, FastAPI, and Supabase. I also help businesses get up and running on Shopify — store setup, theme customization, and third-party integrations.
I'm the kind of person who works well independently, communicates clearly, and doesn't disappear mid-project. If you need someone to untangle messy infrastructure, build a deployment pipeline from scratch, ship an AI-powered product, or launch your online store — I'd love to hear about it.
Let's talk.
Steps for completing your project
After purchasing the project, send requirements so Mohamed can start the project.
Delivery time starts when Mohamed receives requirements from you.
Mohamed works on your project following the steps below.
Revisions may occur after the delivery date.
Assess & Design Architecture
I review your AI app, model requirements, and infra needs. Design the containerization, deployment, and scaling strategy for your specific use case.
Containerize & Deploy
I write optimized Dockerfiles, set up K8s deployment with GPU support if needed, configure CI/CD pipelines, and deploy to your cloud.