From Local Scripts to Edge Deployments: Building Production-Grade AI Infrastructure

#ai #aws #mlops #cloud

Moving Beyond API Wrappers

Building a local prototype with an LLM API takes an hour. Running it in an enterprise environment—handling traffic spikes, controlling costs, and enforcing security guardrails—is a completely different challenge.

As an AI Infrastructure & MLOps Engineer, I bridge the gap between foundation models and scalable cloud architecture. Here are the core lessons I learned architecting and deploying three cloud platforms in 30 days, including CORA, an enterprise AI chatbot with strict constraint guardrails.

The Serverless Edge Stack

To achieve high availability, low latency, and automated deployments, I leveraged:

AWS (EC2, S3, IAM): Core compute and infrastructure primitives.
Vercel & Cloudflare: Global CDN delivery and edge function execution.
Claude API + Guardrails: Secure orchestration preventing prompt injection.
Resend: Automated transactional data and email pipelines.
Terraform (IaC): Reproducible, programmatic environment provisioning.

3 Critical MLOps Lessons

1. Guardrails Belong at the Infrastructure Level
UI prompt engineering isn't enough. Guardrails must be enforced at the backend API layer before queries ever hit the model. Implementing constraint-checking middleware on CORA kept system prompts inviolable while maintaining fast response times.

2. Strict FinOps & Latency Management
Agentic workflows can spike API costs through infinite loops. Edge caching, strict timeout policies, and token budgets are essential for keeping cloud spend predictable.

3. Edge-First Deployments
Shifting state management and routing to edge networks minimizes cold starts, delivering near-instantaneous responses globally.

What’s Next?

I’m currently mastering enterprise architectures and preparing for my AWS Machine Learning Engineer – Associate (MLA-C02) and HashiCorp Terraform certifications.

You can test my interactive AI projects and view my architecture setups at aashishsingh.me.

What are your go-to tools for hosting production AI agents? Let’s discuss in the comments below!