Landing a $200K AI Job in 90 Days? First Look at These 10 GitHub Repos, Then Read the Comments

An X post breaks down the “$200K AI engineer” into two numbers: 90 days and 10 GitHub repositories. The poster, Divyanshi, says that if you want to land a role like this in 90 days, she wouldn’t go get a degree—she’d master this list.

The repositories listed in the post cover the backbone of today’s AI application engineering: RAG, agents, multi-agent systems, local models, MCP, vector databases, system design, and Claude Code. For people with a development background, it’s a usable learning map; for readers without a technical background, it can also serve as a keyword list for the AI application layer.

1. awesome-llm-apps
   The poster calls it a production AI playbook; it includes runnable code for RAG, agents, and multimodal apps, and she says it has 106K+ stars.

2. LangChain
   The foundational framework. The post says Klarna, Replit, Elastic, and most AI startups use it in production.

3. LangGraph
   An orchestration layer for production agents. The post says this is a common skill in senior AI engineer job descriptions.

4. CrewAI
   A multi-agent coordination framework. The post says it’s the first framework most Fortune 500 teams try.

5. Ollama
   Run open-source LLMs on your own machine. For people who want to understand how models work, this is one of the fastest paths.

6. awesome-mcp-servers
   MCP-related resources. The post says MCP is the standard adopted by major AI labs in 2026, and that mastering it can put an engineer ahead of 99% of people. This percentage is the poster’s claim and shouldn’t be treated as a statistical conclusion.

7. Qdrant
   A vector database commonly used for production-grade RAG. Embeddings and semantic search are almost indispensable for AI roles.

8. AI-Agents-for-Beginners
   Microsoft’s free 12-lesson agent course. It includes real code, exercises, and interview preparation materials.

9. system-design-primer
   Production AI ultimately comes down to system design. The post says this is the repository FAANG engineers use to prepare for interviews.

10. awesome-claude-code
    A tool playbook for Claude Code. The post says it’s used by FAANG, OpenAI, Anthropic, and most YC startups.

The list itself is informative. It doesn’t start with “machine learning theory” but points directly to application engineering: integrating models into products, building retrieval, orchestrating tools, handling state, deploying vector databases, and understanding system boundaries. These are indeed the things AI application teams deal with every day.

But putting 90 days and $200K together is still recruitment-ad language. Mastering repositories doesn’t mean you can deliver projects, and being able to deliver projects doesn’t mean you can pass interviews. Employers will look at code, system design, and collaboration records, and they’ll verify whether you’ve actually done the work.

There are also different voices in the comments. Some praise the share, some ask which repository to start with, and someone says outright: if you could really do it, you wouldn’t be here copy-pasting this stuff. It’s a harsh line, but it reminds us of a fact: you can bookmark a list, but you can’t bookmark ability.

The poster writes at the end that the market doesn’t care where you learn—only whether you can ship. That judgment is closer to reality than “10 repositories.” For ordinary readers, picking one repository and getting a small project working is more useful than saving all 10 links to a bookmarks folder. For AI practitioners, this list is good for filling in gaps, but not for treating as a 90-day crash-course contract.