You will get an AI app production-readiness review and action plan
Rising Talent
Project details
You will get a practical production-readiness review for your AI app, backend, API, cloud deployment, and LLM workflow.
I will review your current setup and identify the risks, gaps, and highest-impact improvements needed to move from prototype to a more reliable production system. The review can cover backend architecture, API design, deployment flow, environment setup, logging, error handling, secrets/configuration, cost controls, and AI workflow boundaries.
The final deliverable is a clear action plan with prioritized findings, recommended fixes, and next steps your team can actually implement.
I will review your current setup and identify the risks, gaps, and highest-impact improvements needed to move from prototype to a more reliable production system. The review can cover backend architecture, API design, deployment flow, environment setup, logging, error handling, secrets/configuration, cost controls, and AI workflow boundaries.
The final deliverable is a clear action plan with prioritized findings, recommended fixes, and next steps your team can actually implement.
AI Development Type
Knowledge Representation, Model Tuning, Software MaintenanceAI Tools
MLflow, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$250
|
Standard
$750
|
Advanced
$1,500
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 10 days |
Number of Revisions | 1 | 2 | 2 |
AI Model Integration | - | - | |
Detailed Code Comments | - | - | |
Knowledge Graph | - | - | - |
Model Documentation | - | - | - |
Ontology | - | - | - |
Source Code | - | - | |
Taxonomy | - | - | - |
Optional add-ons
You can add these on the next page.
Additional Revision
+$100Frequently asked questions
About Can
AI Automation & Backend Engineer | Python, MCP, Agents, Cloud
Aliso Viejo, United States - 4:08 am local time
My work sits at the intersection of AI systems, Python backend engineering, and cloud infrastructure. I build practical AI applications that connect LLMs to real business workflows: internal tools, API integrations, retrieval pipelines, agentic workflows, MCP servers, and orchestration layers.
With 6+ years across cloud-native platforms and backend systems, I can help you design, build, deploy, and harden AI-powered software so it is usable beyond the demo stage.
I can help with:
• AI workflow automation and agentic systems
• MCP servers and tool-calling integrations
• Python Flask/FastAPI backend services
• RAG pipelines and structured LLM workflows
• API integrations and internal automation tools
• Cloud deployment on Azure, Google Cloud, Firebase, and Cloud Run
• CI/CD, logging, monitoring, and production-readiness improvements
I’ve built governed MCP servers in Python/Flask, designed agentic infrastructure using RAG and LLM orchestration, and shipped LatentWire, an AI market intelligence product built across backend services, AI workflows, and cloud deployment.
I’m a strong fit if you have an AI prototype, internal workflow, or backend system that needs to become reliable, maintainable, and production-ready.
Steps for completing your project
After purchasing the project, send requirements so Can can start the project.
Delivery time starts when Can receives requirements from you.
Can works on your project following the steps below.
Revisions may occur after the delivery date.
Review requirements and access
I review the app/repo details, deployment notes, AI workflow summary, and any specific concerns you want prioritized.
Assess backend, AI workflow, and deployment readiness
I evaluate the architecture, API flow, LLM usage, cloud/deployment setup, logging, reliability, cost controls, and operational risks.