Full Stack MERN to AI Engineer: My Journey
by Hamzah Ejaz, Software Engineer
Three years ago, I was a MERN stack developer building traditional web applications. Today, I architect AI-powered platforms integrating GPT-4, Perplexity AI, and Deepgram. Here's how I made the transition.
The Catalyst
Working on traditional CRUD applications, I noticed AI was transforming software. ChatGPT launched, and I realized: developers who can bridge AI capabilities with practical applications will be invaluable.
Phase 1: Foundations (Months 1-3)
Learning Path
- Python basics (coming from JavaScript)
- OpenAI API documentation (playground experiments)
- Prompt engineering fundamentals
- Basic ML concepts (not deep learning, just understanding)
First Project
Built a simple ChatGPT wrapper for customer support - taught me:
- API integration basics
- Token management
- Cost optimization
- Real-world limitations
// My first AI integration
const response = await openai.chat.completions.create({
model: 'gpt-3.5-turbo',
messages: [{ role: 'user', content: query }],
})Phase 2: Practical Applications (Months 4-8)
Skills Acquired
- Advanced prompt engineering
- Embedding and semantic search
- RAG (Retrieval Augmented Generation)
- Fine-tuning basics
- Cost management at scale
Project: Internal Knowledge Base
- Embedded company docs with OpenAI embeddings
- Built semantic search with vector databases
- Implemented context-aware Q&A
Impact: Reduced support ticket resolution time by 40%
Phase 3: Production AI (Months 9-12)
Enterprise Integration
- Multi-AI orchestration (GPT-4 + Perplexity + Deepgram)
- Real-time processing pipelines
- Error handling for AI APIs
- Monitoring and observability
EVA Meet Project
This was my breakthrough - architecting an AI platform from scratch:
- Real-time transcription
- Fact-checking pipeline
- Intelligent summarization
- WebSocket infrastructure
Key Lesson: AI engineering isn't just API calls - it's architecture.
What Differentiated Me
1. Full-Stack Background
My React/Node.js experience was crucial:
- Built production-ready UIs
- Handled real-time data flows
- Understood scalability
2. Product Thinking
Focused on business value, not just technology:
- Measured impact (40% time savings, 95% accuracy)
- Understood user needs
- Balanced cost vs. capability
3. Practical Over Theoretical
Didn't need PhD-level ML knowledge:
- Used existing APIs effectively
- Focused on integration patterns
- Solved real problems
Tools I Mastered
AI Platforms:
- OpenAI (GPT-4, Embeddings, Whisper)
- Perplexity AI
- Deepgram
- Claude (Anthropic)
Vector Databases:
- Pinecone
- Weaviate
- ChromaDB
Frameworks:
- LangChain
- LlamaIndex
- Haystack
Lessons for Aspiring AI Engineers
1. Start Small
Don't build AGI. Build a ChatGPT wrapper. Learn fundamentals.
2. Focus on Integration
Most AI engineering is connecting APIs intelligently, not training models.
3. Understand Limitations
- Token limits
- Cost per request
- Latency constraints
- Hallucinations
4. Read Documentation
OpenAI, Anthropic, and Deepgram docs are excellent. Read them thoroughly.
5. Build in Public
Share projects, learnings, failures. The community is supportive.
Common Misconceptions
"I need to learn ML/Deep Learning first" ❌ Not true for most AI engineering roles
"It's just API calls" ❌ Architecture, orchestration, and error handling are complex
"AI will replace developers" ❌ It amplifies capable developers
My Current Stack
Frontend: React + Next.js + TypeScript
Backend: NestJS + Node.js
AI: OpenAI + Perplexity + Deepgram
Real-time: WebSockets + Socket.io
Data: MongoDB + Redis + Vector DBs
Cloud: AWS + Azure + Firebase
ROI of Transition
Before: Building CRUD apps
- $X salary
- Limited differentiation
- Competitive market
After: AI Engineering
- 30% salary increase
- High demand
- Cutting-edge projects
- Leadership opportunities
Resources That Helped
- Courses: OpenAI Cookbook, DeepLearning.AI
- Communities: r/MachineLearning, AI Discord servers
- Blogs: Simon Willison, Eugene Yan
- Documentation: Always start here
Next Steps in My Journey
- Fine-tuning custom models
- Building AI agents
- Exploring multi-modal AI
- Contributing to open-source AI tools
The transition from full-stack to AI engineer was the best career decision I made. If you're considering it, start today. Build something small, ship it, and iterate.
The future of development is AI-augmented. Be early.