AI Engineering Launchpad: 3 Sessions

AI engineering is one of the fastest-moving areas in tech right now and also one of the most confusing to break into. Most resources teach you how to call an API. Very few show you how to actually architect, build, and ship AI features in production.

I own a production AI chat feature built on LLMs and MCP (Model Context Protocol) at a global product SaaS company, currently live and in public preview. I've designed the architecture, handled incidents, debugged real failures, and made the trade-offs that don't show up in any tutorial.

This 3-session package is for backend engineers who want to make a serious move into AI engineering, not just experiment with it.

What you walk away with:

→ A clear picture of where you fit in the AI engineering landscape

→ Hands-on understanding of MCP architecture and LLM tool integration

→ Honest review of your project or idea with specific improvement suggestions

→ A concrete 3-month roadmap to position yourself as an AI engineer

→ Async support between sessions for questions and code review

What this is not: A course. A bootcamp. This is mentorship from someone actively building production AI, not teaching from a textbook.

Best for: Backend engineers with 2+ years of experience who want to transition into AI engineering or start building LLM-powered features seriously.

Session structure

Session 1: AI landscape & where you fit

We map the AI engineering landscape, LLMs, RAG, agents, and MCP, and figure out exactly where your background fits in. You'll leave knowing which path makes the most sense for your goals and current skills.

Session 2: Hands-on MCP & LLM architecture

We go deep on MCP architecture and LLM tool integration, the same patterns I use in production. Real code structure, real design decisions, real failure patterns. If you have a project, we'll review it directly.

Session 3: Your project review & 3-month roadmap

We review your project or idea with concrete feedback, then build your 3-month roadmap on what to build, what to learn, and how to position yourself as an AI engineer in the job market.