Full-Stack AIÂ Developer Roadmap
How to go from zero to hireable in the new AI job market.
The software industry is changing fast. Junior developer roles are saturated, while companies are investing heavily in AI and urgently hiring developers who can build production-ready applications with large language models.
This roadmap shows you the exact sequence to become a Full-Stack AI Developer, even if you’re starting from zero.
What Is a Full-Stack AI Engineer?
A Full-Stack AI Engineer is not a data scientist, a researcher, or a “prompt engineer.” It’s a developer who can build traditional full-stack applications and integrate AI into them responsibly and effectively.
This is an emerging role and the title may change but the core skills will remain
AI developers can:
- Build React and Next.js frontends
- Write clean TypeScript
- Create APIs using Node, Express, or Next.js routes
- Work with SQL and Postgres
- Integrate LLM APIs (OpenAI, Anthropic, Gemini)
- Use vector databases for search
- Build RAG (Retrieval-Augmented Generation) systems
- Implement streaming UIs, tool calling, and basic agent workflows
- Deploy to Vercel and monitor performance
Phase 0: Core Web Development Skills
Before learning AI integration, you must be able to build a standard full-stack app. This includes:
- HTML, CSS, JavaScript
- React fundamentals
- TypeScript basics
- APIs (Node, Express, Next.js API routes)
- SQL, Postgres
- Git and GitHub
- Deploying to Vercel or Netlify
Beginner-friendly resources:
- Parsity Dev30 (JavaScript fundamentals)
- Codecademy HTML/CSS/JS
- MDN Web Docs
- Scrimba JavaScript and React
- React Official Docs
- Parsity Node + Express Starter Kit
- Prisma Docs (SQL ORM)
- NeonDB (Postgres)
Phase 1: Math and Vector Intuition
You do not need advanced math. You only need intuition for:
- Vectors
- Dot product
- Cosine similarity
Resources:
Phase 2: AI APIs and Your First LLM App
The goal is to learn how to:
- Call OpenAI, Anthropic, or Gemini APIs
- Use basic prompting
- Work with tokens and costs
- Build your first chatbot using plain JavaScript
Beginner starter project:
API docs:
Phase 3: Production UX Patterns for AI
AI-powered apps require:
- Streaming responses
- Retry logic
- Fallbacks for slow/failed requests
- Observability
- Basic security around user input
Recommended tools:
Phase 4: Agents and Orchestration
Learn how to build workflows instead of single-model calls:
- Tool calling
- Routing models
- Selector/aggregator/refiner patterns
- Multi-step reasoning
Beginner guide:
Phase 5: Build a Full RAG Application (Capstone)
This is the project that proves you can build real AI systems.
You will:
- Scrape or upload data
- Chunk the data
- Generate embeddings
- Store vectors in a vector database
- Perform semantic search
- Feed retrieved context into the LLM
- Stream the results to the UI
- Deploy the final app
RAG resources:
Phase 6: Career, Portfolio, and Personal Brand
Skills matter. Visibility matters equally.
Share:
- Daily learnings
- Small demos
- Progress updates
- Short code walkthroughs
- Mini experiments
Resource:
Docs and Tools Index
- OpenAI API Docs
- Anthropic API Docs
- Gemini Docs
- Pinecone Docs
- Qdrant Docs
- Weaviate Docs
- Vercel AI SDK
- Prisma Docs
- Neon Postgres
Final Thought
This roadmap gives you the exact sequence to go from zero to hireable as a Full-Stack AI Engineer.
Focus on:
- Full-stack fundamentals
- Vector intuition
- LLM API integration
- Production UX patterns
- Agent workflows
- One strong RAG project
- Consistent public portfolio
This is the skillset that is on the rise and likely will be even AFTER the hype-cycle cools down.
