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ai-engineer-path

Guide learners through a six-step framework across three AI engineering roles.

Updated Apr 4, 2026
Search Tags:#langchain#mlops#systems-design#learning-path#ai-engineering#career-path
One-click install
npx skills add https://github.com/susantosanto/config-opencode --skill ai-engineer-path
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-engineer-path
Source: https://github.com/susantosanto/config-opencode/tree/main/skills/ai-engineer-path
Command: npx skills add https://github.com/susantosanto/config-opencode --skill ai-engineer-path

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Learners gain a coherent, scalable path to become top AI engineers, avoiding fragmented tutorials and ad-hoc project work.

Core Features & Use Cases

  • A formal progression across three roles: Problem Solver, System Architect, AI Orchestrator, guided by a six-step framework.
  • A 90-day roadmap with project milestones to build production-ready AI capabilities.
  • Real-world use cases like building a production RAG system or an enterprise AI platform with multi-agent orchestration.

Quick Start

Begin with Phase 1 Python fundamentals and follow the six-step roadmap to progress through the three roles.

Frequently Asked Questions about ai-engineer-path

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I transition to an AI engineering career path without getting lost in fragmented tutorials?▼

An AI engineering career path requires a structured progression across three roles: Problem Solver, System Architect, and AI Orchestrator. A six-step framework with a 90-day technical roadmap guides you through project milestones to build production-ready capabilities.

What is the best way to learn Langchain and multi-agent workflows for enterprise AI platforms?▼

Learning Langchain and multi-agent workflows is best achieved through real-world use cases like building a production RAG system. The structured roadmap guides you from Python fundamentals to orchestrating complex multi-agent enterprise platforms.

Do I need Python fundamentals before starting an AI engineering roadmap?▼

Python fundamentals are required before starting the AI engineering roadmap. Phase 1 begins with Python basics, followed by a six-step progression to build comprehensive skill stacking for production-readiness.

How to build a production RAG system as part of an AI engineering learning path?▼

To build a production RAG system within an AI engineering learning path, you follow project milestones in the 90-day roadmap. It transitions you from a Problem Solver to an AI Orchestrator, incorporating MLOps and multi-agent orchestration for production-readiness.

What roles should I target to become a top AI engineer using a structured learning path?▼

A structured AI engineering learning path targets three roles: Problem Solver, System Architect, and AI Orchestrator. This progression ensures comprehensive skill stacking, moving from basic Python to advanced multi-agent workflows and systems design.

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