The LLM Developer Career Path

Why TypeScript Engineers Are Perfectly Positioned for the Next Wave of AI

There is a new software engineering role emerging from the dumpster fire that is the tech market.

Maybe “new role” is a bit much.

It’s more like full stack with an AI twist.

Let’s get one thing straight. You will not be building GPT-5.

That ship has sailed. OpenAI, Anthropic, Google, Meta, and a few others are pouring billions into model training. And yet some AI bros are out here with a MacBook Air trying to train a trillion-parameter model over free Wi-Fi.

There is some investor dumb enough to fund it. Guaranteed.

What you are doing — or should be doing — is building around the model.

The Python Illusion

Python has the AI clout. Fine. Let it.

The ecosystem is great for researchers and data scientists.

Meanwhile, most useful LLM applications today look like this:

• A React frontend
• An API that calls OpenAI or Anthropic
• A database (Postgres, Supabase, Firebase)
• And increasingly, a vector database behind the scenes (vector databases have become the unofficial choice for AI)

If you know how to build a full stack app, you’re 80 percent of the way to building a useful AI-powered app. You just need to understand the new pieces to the AI puzzle.

What You Actually Need to Know

Here’s the technologies that will define the LLM engineer career path:

1. Embeddings: Words are turned into vectors. You don’t need to do this by hand, just understand that when you call OpenAI’s embedding endpoint, it returns a list of numbers. These numbers represent meaning.
2. Cosine Similarity and Dot Product: These are the math tricks that help you say “This chunk of text is close to that chunk of text.” That’s how search works in AI. You don’t need to be a mathlete. You need to understand the concept.
3. Vector Databases: These are specialized databases that store text and their vector representations. You query them with vectorized text, and they return the closest matches. Pinecone, Weaviate, and Chroma are the big players here.
4. RAG: Retrieval-Augmented Generation: This is the most popular real-world use case for LLMs. RAG means you grab your own data and send it alongside the prompt to the model so it can generate better answers.

Over 65 percent of enterprise LLM implementations in 2024 used RAG instead of fine-tuning

[source]

Fine-tuning is expensive, brittle, and often unnecessary. RAG is boring, popular and incredibly useful.

Why TypeScript Devs Are in the Sweet Spot

Let’s spell this out.

If you’re building anything that:

• Scrapes content
• Stores it
• Lets users query it
• Calls OpenAI
• Streams back a dynamic response

Then TypeScript probably makes a lot more sense than Python.

Users expect highly interactive, live-streaming, beautifully designed interfaces… you know, kinda like ChatGPT.

If you’re using ReactJS, Next.js and TypeScript then you’re most of the way there.

But there’s another reason TypeScript devs have an edge — and it’s not just about UI.

LLMs Love Structure

One of the most underrated truths about working with LLMs is that they give better, more consistent responses when your input is structured — and when you can parse the output safely and predictably.

This is where TypeScript shines.

When you’re handling multi-part responses, generating tool invocations, or chaining function calls, having clear types and interfaces actually helps both the AI and your codebase.

Clean types. Safe parsing. Real-time feedback. This is the kind of thing modern AI products need and it’s what TypeScript enables out of the box.

So How Do You Get Started?

Here’s your five-step plan. No fluff. No AI fairy dust.

1. Scrape some data: Use firecrawl.dev or build your own simple scraper. Target something public, useful, and structured.
2. Get embeddings: Use OpenAI’s embedding API. It takes in text and returns vectors.
3. Store them in a vector database: Start with Pinecone or Chroma. Chroma is free and runs locally if you’re just experimenting.
4. Build a Next.js app that searches and streams: Send the user’s query to OpenAI with the retrieved context. Show the answer as it streams. Vercel’s AI SDK makes this simple(r).
5. Keep uploading more data and iterate: Build a cron job or trigger to regularly update your dataset. Add support for users to bring their own data. Start thinking like a product person, not a prompt engineer.

Final Thoughts

The hype cycle is almost over. The people building real AI products are already moving.

If you’re a TypeScript engineer and you’ve been waiting for permission to get in — this is it.

You don’t need to be a Python expert. You don’t need a PhD. And you don’t need to build your own model.

You need to build a product that uses one.

And the best way to start is by building something simple, useful, and specific.

Your AI career doesn’t start with a paper. It starts with a project.

I really don’t want to make a course. People rarely finish them.

I am making some challenges to teach you these skills in a hands-on way. If you’re interested just sign up for early access here: parsity.io/ai-developer