🚀Full-Stack AIIntermediate
Full-Stack AI Engineer Path
Become a versatile AI engineer who can build end-to-end AI-powered applications. From frontend to backend to ML models - own the entire stack. Perfect for startups and product teams.
18 weeks
9 milestones
0 items
Skills You Will Gain
React/Next.jsPython BackendLLM IntegrationRAG SystemsVector DatabasesAI APIsFull-Stack DeploymentProduct Thinking
Prerequisites
- →Web development basics (HTML, CSS, JavaScript)
- →Python programming
- →Basic understanding of databases
- →Familiarity with APIs
Learning Milestones
1
Modern Frontend Development
Build polished AI-powered user interfaces with React and Next.js.
~22h•0 items
Learning Objectives
- ✓Master React fundamentals and hooks
- ✓Build with Next.js App Router
- ✓Create responsive, accessible UIs with Tailwind
- ✓Handle streaming responses in UI
- ✓Implement real-time updates
- ✓Build chat interfaces and AI interactions
Content coming soon
2
Python Backend Development
Build robust backend services for AI applications.
~20h•0 items
Learning Objectives
- ✓Build APIs with FastAPI
- ✓Handle async operations for AI workloads
- ✓Implement authentication and authorization
- ✓Design scalable API architectures
- ✓Handle file uploads and processing
- ✓Build background job systems
Content coming soon
3
Database Design for AI Apps
Design and implement databases optimized for AI applications.
~15h•0 items
Learning Objectives
- ✓Design schemas for AI applications
- ✓Use PostgreSQL effectively (including pgvector)
- ✓Implement caching with Redis
- ✓Handle vector data alongside relational data
- ✓Optimize database performance
- ✓Handle data migrations
Content coming soon
4
LLM Integration Patterns
Master patterns for integrating LLMs into applications.
~18h•0 items
Learning Objectives
- ✓Integrate OpenAI, Anthropic, and open-source LLMs
- ✓Implement streaming responses
- ✓Handle errors and rate limits gracefully
- ✓Build conversation management systems
- ✓Implement prompt templates and versioning
- ✓Optimize for cost and latency
Content coming soon
5
Building RAG Applications
Build production-ready RAG systems end-to-end.
~22h•0 items
Learning Objectives
- ✓Design document processing pipelines
- ✓Implement vector search with multiple backends
- ✓Build retrieval and generation pipelines
- ✓Create user-friendly document upload UIs
- ✓Handle multi-format documents
- ✓Implement source attribution and citations
Content coming soon
6
AI Agent Integration
Build applications that leverage AI agents for complex tasks.
~18h•0 items
Learning Objectives
- ✓Integrate LangChain agents into apps
- ✓Build tool-using agents with UI feedback
- ✓Handle agent errors gracefully
- ✓Create agent monitoring dashboards
- ✓Implement human-in-the-loop workflows
- ✓Design agent-powered features
Content coming soon
7
Authentication & Multi-tenancy
Implement secure authentication and multi-tenant AI apps.
~15h•0 items
Learning Objectives
- ✓Implement OAuth and JWT authentication
- ✓Design multi-tenant architectures
- ✓Handle API key management
- ✓Implement rate limiting per user
- ✓Build subscription and billing systems
- ✓Handle data isolation
Content coming soon
8
Deployment & DevOps
Deploy and operate AI applications in production.
~18h•0 items
Learning Objectives
- ✓Deploy with Vercel, Railway, or AWS
- ✓Containerize applications with Docker
- ✓Set up CI/CD pipelines
- ✓Implement monitoring and logging
- ✓Handle scaling and load balancing
- ✓Manage environment variables and secrets
Content coming soon
9
Building AI Products
Apply product thinking to AI application development.
~15h•0 items
Learning Objectives
- ✓Identify AI-native product opportunities
- ✓Design AI-first user experiences
- ✓Handle AI uncertainty in UX
- ✓Collect and use user feedback
- ✓Measure AI feature success
- ✓Iterate based on real usage
Content coming soon
Content Summary
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